program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})]
{
    func main<ios17>(tensor<int32, [1]> mel_length, tensor<fp32, [1, 80, 1501]> melspectrogram_features) {
            tensor<fp32, [1025]> proj_bias = const()[name = tensor<string, []>("proj_bias"), val = tensor<fp32, [1025]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [1025, 1024]> proj_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("proj_weight_quantized"), quantized_data = tensor<int8, [1025, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4288))), scale = tensor<fp32, [1025]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1055104))), zero_point = tensor<int8, [1025]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1053952)))];
            tensor<fp32, [256]> encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1059328)))];
            tensor<fp32, [256, 1, 3, 3]> encoder_pre_encode_conv_0_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_0_weight_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1060416))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1063104))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062784)))];
            tensor<fp32, [256]> encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1064192)))];
            tensor<fp32, [256, 1, 3, 3]> encoder_pre_encode_conv_2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_2_weight_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1065280))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1067968))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1067648)))];
            tensor<fp32, [256]> encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1069056)))];
            tensor<fp32, [256, 256, 1, 1]> encoder_pre_encode_conv_3_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_3_weight_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1070144))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1136064))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1135744)))];
            tensor<fp32, [256]> encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1137152)))];
            tensor<fp32, [256, 1, 3, 3]> encoder_pre_encode_conv_5_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_5_weight_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1138240))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1140928))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1140608)))];
            tensor<fp32, [256]> encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1142016)))];
            tensor<fp32, [256, 256, 1, 1]> encoder_pre_encode_conv_6_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_6_weight_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1143104))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209024))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1208704)))];
            tensor<fp32, [1024]> encoder_pre_encode_out_bias = const()[name = tensor<string, []>("encoder_pre_encode_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210112)))];
            tensor<fp32, [1024, 2560]> encoder_pre_encode_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_out_weight_quantized"), quantized_data = tensor<int8, [1024, 2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1214272))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3836864))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3835776)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_0_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3841024)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_0_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3845184)))];
            tensor<fp32, [4096]> encoder_layers_0_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_0_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3849344)))];
            tensor<fp32, [4096, 1024]> encoder_layers_0_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3865792))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8064320))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8060160)))];
            tensor<fp32, [1024]> encoder_layers_0_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_0_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8080768)))];
            tensor<fp32, [1024, 4096]> encoder_layers_0_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8084928))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12280384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12279296)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_0_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12284544)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_0_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12288704)))];
            tensor<fp32, [8, 128]> encoder_layers_0_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_0_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12292864)))];
            tensor<fp32, [8, 128]> encoder_layers_0_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_0_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12297024)))];
            tensor<fp32, [1024]> encoder_layers_0_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_0_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12301184)))];
            tensor<fp32, [1024, 1024]> encoder_layers_0_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12305344))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13355072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13353984)))];
            tensor<fp32, [1024]> encoder_layers_0_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_0_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13359232)))];
            tensor<fp32, [1024, 1024]> encoder_layers_0_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13363392))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14413120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14412032)))];
            tensor<fp32, [1024]> encoder_layers_0_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_0_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14417280)))];
            tensor<fp32, [1024, 1024]> encoder_layers_0_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14421440))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15471168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15470080)))];
            tensor<fp32, [1024]> encoder_layers_0_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_0_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15475328)))];
            tensor<fp32, [1024, 1024]> encoder_layers_0_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15479488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16529216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16528128)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_0_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16533376)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_0_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16537536)))];
            tensor<fp32, [2048]> encoder_layers_0_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_0_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16541696)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_0_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16549952))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18649280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18647168)))];
            tensor<fp32, [1024]> encoder_layers_0_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_0_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18657536)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_0_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18661696))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19711424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19710336)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_0_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19715584)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_0_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19719744)))];
            tensor<fp32, [4096]> encoder_layers_0_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_0_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19723904)))];
            tensor<fp32, [4096, 1024]> encoder_layers_0_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19740352))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23938880))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23934720)))];
            tensor<fp32, [1024]> encoder_layers_0_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_0_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23955328)))];
            tensor<fp32, [1024, 4096]> encoder_layers_0_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_0_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23959488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28154944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28153856)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_0_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28159104)))];
            tensor<fp32, [1024]> encoder_layers_0_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_0_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28163264)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_1_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28167424)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_1_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28171584)))];
            tensor<fp32, [4096]> encoder_layers_1_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_1_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28175744)))];
            tensor<fp32, [4096, 1024]> encoder_layers_1_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28192192))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32390720))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32386560)))];
            tensor<fp32, [1024]> encoder_layers_1_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_1_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32407168)))];
            tensor<fp32, [1024, 4096]> encoder_layers_1_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32411328))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36606784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36605696)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_1_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36610944)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_1_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36615104)))];
            tensor<fp32, [8, 128]> encoder_layers_1_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_1_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36619264)))];
            tensor<fp32, [8, 128]> encoder_layers_1_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_1_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36623424)))];
            tensor<fp32, [1024]> encoder_layers_1_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_1_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36627584)))];
            tensor<fp32, [1024, 1024]> encoder_layers_1_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36631744))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37681472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37680384)))];
            tensor<fp32, [1024]> encoder_layers_1_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_1_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37685632)))];
            tensor<fp32, [1024, 1024]> encoder_layers_1_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37689792))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38739520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38738432)))];
            tensor<fp32, [1024]> encoder_layers_1_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_1_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38743680)))];
            tensor<fp32, [1024, 1024]> encoder_layers_1_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38747840))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39797568))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39796480)))];
            tensor<fp32, [1024]> encoder_layers_1_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_1_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39801728)))];
            tensor<fp32, [1024, 1024]> encoder_layers_1_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39805888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40855616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40854528)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_1_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40859776)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_1_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40863936)))];
            tensor<fp32, [2048]> encoder_layers_1_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_1_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40868096)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_1_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40876352))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42975680))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42973568)))];
            tensor<fp32, [1024]> encoder_layers_1_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_1_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42983936)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_1_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42988096))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44037824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44036736)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_1_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44041984)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_1_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44046144)))];
            tensor<fp32, [4096]> encoder_layers_1_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_1_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44050304)))];
            tensor<fp32, [4096, 1024]> encoder_layers_1_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44066752))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48265280))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48261120)))];
            tensor<fp32, [1024]> encoder_layers_1_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_1_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48281728)))];
            tensor<fp32, [1024, 4096]> encoder_layers_1_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_1_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48285888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52481344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52480256)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_1_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52485504)))];
            tensor<fp32, [1024]> encoder_layers_1_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_1_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52489664)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_2_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52493824)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_2_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52497984)))];
            tensor<fp32, [4096]> encoder_layers_2_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_2_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52502144)))];
            tensor<fp32, [4096, 1024]> encoder_layers_2_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52518592))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56717120))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56712960)))];
            tensor<fp32, [1024]> encoder_layers_2_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_2_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56733568)))];
            tensor<fp32, [1024, 4096]> encoder_layers_2_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56737728))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60933184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60932096)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_2_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60937344)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_2_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60941504)))];
            tensor<fp32, [8, 128]> encoder_layers_2_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_2_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60945664)))];
            tensor<fp32, [8, 128]> encoder_layers_2_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_2_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60949824)))];
            tensor<fp32, [1024]> encoder_layers_2_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_2_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60953984)))];
            tensor<fp32, [1024, 1024]> encoder_layers_2_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60958144))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62007872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62006784)))];
            tensor<fp32, [1024]> encoder_layers_2_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_2_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62012032)))];
            tensor<fp32, [1024, 1024]> encoder_layers_2_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62016192))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63065920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63064832)))];
            tensor<fp32, [1024]> encoder_layers_2_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_2_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63070080)))];
            tensor<fp32, [1024, 1024]> encoder_layers_2_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63074240))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64123968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64122880)))];
            tensor<fp32, [1024]> encoder_layers_2_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_2_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64128128)))];
            tensor<fp32, [1024, 1024]> encoder_layers_2_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64132288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65182016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65180928)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_2_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65186176)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_2_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65190336)))];
            tensor<fp32, [2048]> encoder_layers_2_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_2_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65194496)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_2_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65202752))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67302080))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67299968)))];
            tensor<fp32, [1024]> encoder_layers_2_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_2_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67310336)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_2_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67314496))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68364224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68363136)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_2_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68368384)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_2_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68372544)))];
            tensor<fp32, [4096]> encoder_layers_2_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_2_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68376704)))];
            tensor<fp32, [4096, 1024]> encoder_layers_2_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68393152))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72591680))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72587520)))];
            tensor<fp32, [1024]> encoder_layers_2_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_2_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72608128)))];
            tensor<fp32, [1024, 4096]> encoder_layers_2_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_2_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72612288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76807744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76806656)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_2_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76811904)))];
            tensor<fp32, [1024]> encoder_layers_2_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_2_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76816064)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_3_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76820224)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_3_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76824384)))];
            tensor<fp32, [4096]> encoder_layers_3_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_3_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76828544)))];
            tensor<fp32, [4096, 1024]> encoder_layers_3_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76844992))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81043520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81039360)))];
            tensor<fp32, [1024]> encoder_layers_3_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_3_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81059968)))];
            tensor<fp32, [1024, 4096]> encoder_layers_3_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81064128))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85259584))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85258496)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_3_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85263744)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_3_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85267904)))];
            tensor<fp32, [8, 128]> encoder_layers_3_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_3_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85272064)))];
            tensor<fp32, [8, 128]> encoder_layers_3_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_3_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85276224)))];
            tensor<fp32, [1024]> encoder_layers_3_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_3_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85280384)))];
            tensor<fp32, [1024, 1024]> encoder_layers_3_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85284544))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86334272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86333184)))];
            tensor<fp32, [1024]> encoder_layers_3_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_3_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86338432)))];
            tensor<fp32, [1024, 1024]> encoder_layers_3_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86342592))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87392320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87391232)))];
            tensor<fp32, [1024]> encoder_layers_3_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_3_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87396480)))];
            tensor<fp32, [1024, 1024]> encoder_layers_3_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87400640))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88450368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88449280)))];
            tensor<fp32, [1024]> encoder_layers_3_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_3_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88454528)))];
            tensor<fp32, [1024, 1024]> encoder_layers_3_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88458688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89508416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89507328)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_3_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89512576)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_3_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89516736)))];
            tensor<fp32, [2048]> encoder_layers_3_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_3_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89520896)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_3_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89529152))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91628480))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91626368)))];
            tensor<fp32, [1024]> encoder_layers_3_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_3_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91636736)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_3_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91640896))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92690624))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92689536)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_3_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92694784)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_3_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92698944)))];
            tensor<fp32, [4096]> encoder_layers_3_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_3_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92703104)))];
            tensor<fp32, [4096, 1024]> encoder_layers_3_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92719552))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96918080))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96913920)))];
            tensor<fp32, [1024]> encoder_layers_3_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_3_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96934528)))];
            tensor<fp32, [1024, 4096]> encoder_layers_3_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_3_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96938688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101134144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101133056)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_3_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101138304)))];
            tensor<fp32, [1024]> encoder_layers_3_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_3_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101142464)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_4_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101146624)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_4_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101150784)))];
            tensor<fp32, [4096]> encoder_layers_4_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_4_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101154944)))];
            tensor<fp32, [4096, 1024]> encoder_layers_4_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101171392))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105369920))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105365760)))];
            tensor<fp32, [1024]> encoder_layers_4_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_4_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105386368)))];
            tensor<fp32, [1024, 4096]> encoder_layers_4_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105390528))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109585984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109584896)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_4_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109590144)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_4_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109594304)))];
            tensor<fp32, [8, 128]> encoder_layers_4_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_4_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109598464)))];
            tensor<fp32, [8, 128]> encoder_layers_4_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_4_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109602624)))];
            tensor<fp32, [1024]> encoder_layers_4_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_4_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109606784)))];
            tensor<fp32, [1024, 1024]> encoder_layers_4_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109610944))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110660672))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110659584)))];
            tensor<fp32, [1024]> encoder_layers_4_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_4_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110664832)))];
            tensor<fp32, [1024, 1024]> encoder_layers_4_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110668992))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111718720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111717632)))];
            tensor<fp32, [1024]> encoder_layers_4_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_4_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111722880)))];
            tensor<fp32, [1024, 1024]> encoder_layers_4_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111727040))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112776768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112775680)))];
            tensor<fp32, [1024]> encoder_layers_4_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_4_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112780928)))];
            tensor<fp32, [1024, 1024]> encoder_layers_4_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112785088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113834816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113833728)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_4_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113838976)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_4_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113843136)))];
            tensor<fp32, [2048]> encoder_layers_4_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_4_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113847296)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_4_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113855552))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115954880))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115952768)))];
            tensor<fp32, [1024]> encoder_layers_4_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_4_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115963136)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_4_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115967296))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117017024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117015936)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_4_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117021184)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_4_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117025344)))];
            tensor<fp32, [4096]> encoder_layers_4_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_4_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117029504)))];
            tensor<fp32, [4096, 1024]> encoder_layers_4_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117045952))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121244480))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121240320)))];
            tensor<fp32, [1024]> encoder_layers_4_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_4_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121260928)))];
            tensor<fp32, [1024, 4096]> encoder_layers_4_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_4_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121265088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125460544))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125459456)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_4_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125464704)))];
            tensor<fp32, [1024]> encoder_layers_4_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_4_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125468864)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_5_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125473024)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_5_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125477184)))];
            tensor<fp32, [4096]> encoder_layers_5_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_5_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125481344)))];
            tensor<fp32, [4096, 1024]> encoder_layers_5_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125497792))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129696320))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129692160)))];
            tensor<fp32, [1024]> encoder_layers_5_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_5_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129712768)))];
            tensor<fp32, [1024, 4096]> encoder_layers_5_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129716928))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133912384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133911296)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_5_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133916544)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_5_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133920704)))];
            tensor<fp32, [8, 128]> encoder_layers_5_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_5_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133924864)))];
            tensor<fp32, [8, 128]> encoder_layers_5_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_5_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133929024)))];
            tensor<fp32, [1024]> encoder_layers_5_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_5_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133933184)))];
            tensor<fp32, [1024, 1024]> encoder_layers_5_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133937344))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134987072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134985984)))];
            tensor<fp32, [1024]> encoder_layers_5_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_5_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134991232)))];
            tensor<fp32, [1024, 1024]> encoder_layers_5_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134995392))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136045120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136044032)))];
            tensor<fp32, [1024]> encoder_layers_5_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_5_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136049280)))];
            tensor<fp32, [1024, 1024]> encoder_layers_5_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136053440))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137103168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137102080)))];
            tensor<fp32, [1024]> encoder_layers_5_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_5_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137107328)))];
            tensor<fp32, [1024, 1024]> encoder_layers_5_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137111488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138161216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138160128)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_5_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138165376)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_5_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138169536)))];
            tensor<fp32, [2048]> encoder_layers_5_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_5_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138173696)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_5_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138181952))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140281280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140279168)))];
            tensor<fp32, [1024]> encoder_layers_5_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_5_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140289536)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_5_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140293696))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141343424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141342336)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_5_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141347584)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_5_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141351744)))];
            tensor<fp32, [4096]> encoder_layers_5_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_5_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141355904)))];
            tensor<fp32, [4096, 1024]> encoder_layers_5_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141372352))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145570880))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145566720)))];
            tensor<fp32, [1024]> encoder_layers_5_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_5_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145587328)))];
            tensor<fp32, [1024, 4096]> encoder_layers_5_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_5_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145591488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149786944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149785856)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_5_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149791104)))];
            tensor<fp32, [1024]> encoder_layers_5_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_5_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149795264)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_6_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149799424)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_6_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149803584)))];
            tensor<fp32, [4096]> encoder_layers_6_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_6_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149807744)))];
            tensor<fp32, [4096, 1024]> encoder_layers_6_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149824192))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154022720))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154018560)))];
            tensor<fp32, [1024]> encoder_layers_6_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_6_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154039168)))];
            tensor<fp32, [1024, 4096]> encoder_layers_6_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154043328))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158238784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158237696)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_6_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158242944)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_6_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158247104)))];
            tensor<fp32, [8, 128]> encoder_layers_6_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_6_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158251264)))];
            tensor<fp32, [8, 128]> encoder_layers_6_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_6_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158255424)))];
            tensor<fp32, [1024]> encoder_layers_6_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_6_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158259584)))];
            tensor<fp32, [1024, 1024]> encoder_layers_6_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158263744))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159313472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159312384)))];
            tensor<fp32, [1024]> encoder_layers_6_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_6_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159317632)))];
            tensor<fp32, [1024, 1024]> encoder_layers_6_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159321792))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160371520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160370432)))];
            tensor<fp32, [1024]> encoder_layers_6_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_6_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160375680)))];
            tensor<fp32, [1024, 1024]> encoder_layers_6_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160379840))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161429568))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161428480)))];
            tensor<fp32, [1024]> encoder_layers_6_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_6_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161433728)))];
            tensor<fp32, [1024, 1024]> encoder_layers_6_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161437888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162487616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162486528)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_6_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162491776)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_6_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162495936)))];
            tensor<fp32, [2048]> encoder_layers_6_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_6_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162500096)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_6_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162508352))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164607680))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164605568)))];
            tensor<fp32, [1024]> encoder_layers_6_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_6_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164615936)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_6_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164620096))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165669824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165668736)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_6_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165673984)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_6_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165678144)))];
            tensor<fp32, [4096]> encoder_layers_6_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_6_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165682304)))];
            tensor<fp32, [4096, 1024]> encoder_layers_6_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165698752))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169897280))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169893120)))];
            tensor<fp32, [1024]> encoder_layers_6_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_6_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169913728)))];
            tensor<fp32, [1024, 4096]> encoder_layers_6_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_6_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169917888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174113344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174112256)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_6_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174117504)))];
            tensor<fp32, [1024]> encoder_layers_6_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_6_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174121664)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_7_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174125824)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_7_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174129984)))];
            tensor<fp32, [4096]> encoder_layers_7_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_7_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174134144)))];
            tensor<fp32, [4096, 1024]> encoder_layers_7_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174150592))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178349120))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178344960)))];
            tensor<fp32, [1024]> encoder_layers_7_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_7_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178365568)))];
            tensor<fp32, [1024, 4096]> encoder_layers_7_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178369728))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182565184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182564096)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_7_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182569344)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_7_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182573504)))];
            tensor<fp32, [8, 128]> encoder_layers_7_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_7_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182577664)))];
            tensor<fp32, [8, 128]> encoder_layers_7_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_7_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182581824)))];
            tensor<fp32, [1024]> encoder_layers_7_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_7_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182585984)))];
            tensor<fp32, [1024, 1024]> encoder_layers_7_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182590144))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183639872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183638784)))];
            tensor<fp32, [1024]> encoder_layers_7_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_7_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183644032)))];
            tensor<fp32, [1024, 1024]> encoder_layers_7_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183648192))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184697920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184696832)))];
            tensor<fp32, [1024]> encoder_layers_7_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_7_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184702080)))];
            tensor<fp32, [1024, 1024]> encoder_layers_7_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184706240))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185755968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185754880)))];
            tensor<fp32, [1024]> encoder_layers_7_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_7_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185760128)))];
            tensor<fp32, [1024, 1024]> encoder_layers_7_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185764288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186814016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186812928)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_7_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186818176)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_7_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186822336)))];
            tensor<fp32, [2048]> encoder_layers_7_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_7_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186826496)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_7_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186834752))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188934080))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188931968)))];
            tensor<fp32, [1024]> encoder_layers_7_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_7_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188942336)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_7_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188946496))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189996224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189995136)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_7_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190000384)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_7_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190004544)))];
            tensor<fp32, [4096]> encoder_layers_7_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_7_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190008704)))];
            tensor<fp32, [4096, 1024]> encoder_layers_7_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190025152))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194223680))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194219520)))];
            tensor<fp32, [1024]> encoder_layers_7_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_7_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194240128)))];
            tensor<fp32, [1024, 4096]> encoder_layers_7_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_7_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194244288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198439744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198438656)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_7_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198443904)))];
            tensor<fp32, [1024]> encoder_layers_7_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_7_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198448064)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_8_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198452224)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_8_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198456384)))];
            tensor<fp32, [4096]> encoder_layers_8_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_8_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198460544)))];
            tensor<fp32, [4096, 1024]> encoder_layers_8_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198476992))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202675520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202671360)))];
            tensor<fp32, [1024]> encoder_layers_8_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_8_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202691968)))];
            tensor<fp32, [1024, 4096]> encoder_layers_8_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202696128))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206891584))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206890496)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_8_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206895744)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_8_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206899904)))];
            tensor<fp32, [8, 128]> encoder_layers_8_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_8_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206904064)))];
            tensor<fp32, [8, 128]> encoder_layers_8_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_8_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206908224)))];
            tensor<fp32, [1024]> encoder_layers_8_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_8_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206912384)))];
            tensor<fp32, [1024, 1024]> encoder_layers_8_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206916544))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207966272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207965184)))];
            tensor<fp32, [1024]> encoder_layers_8_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_8_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207970432)))];
            tensor<fp32, [1024, 1024]> encoder_layers_8_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207974592))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209024320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209023232)))];
            tensor<fp32, [1024]> encoder_layers_8_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_8_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209028480)))];
            tensor<fp32, [1024, 1024]> encoder_layers_8_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209032640))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210082368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210081280)))];
            tensor<fp32, [1024]> encoder_layers_8_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_8_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210086528)))];
            tensor<fp32, [1024, 1024]> encoder_layers_8_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210090688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211140416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211139328)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_8_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211144576)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_8_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211148736)))];
            tensor<fp32, [2048]> encoder_layers_8_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_8_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211152896)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_8_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211161152))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213260480))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213258368)))];
            tensor<fp32, [1024]> encoder_layers_8_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_8_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213268736)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_8_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213272896))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214322624))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214321536)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_8_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214326784)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_8_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214330944)))];
            tensor<fp32, [4096]> encoder_layers_8_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_8_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214335104)))];
            tensor<fp32, [4096, 1024]> encoder_layers_8_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214351552))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218550080))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218545920)))];
            tensor<fp32, [1024]> encoder_layers_8_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_8_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218566528)))];
            tensor<fp32, [1024, 4096]> encoder_layers_8_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_8_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218570688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222766144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222765056)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_8_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222770304)))];
            tensor<fp32, [1024]> encoder_layers_8_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_8_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222774464)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_9_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222778624)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_9_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222782784)))];
            tensor<fp32, [4096]> encoder_layers_9_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_9_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222786944)))];
            tensor<fp32, [4096, 1024]> encoder_layers_9_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222803392))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227001920))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226997760)))];
            tensor<fp32, [1024]> encoder_layers_9_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_9_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227018368)))];
            tensor<fp32, [1024, 4096]> encoder_layers_9_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227022528))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231217984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231216896)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_9_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231222144)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_9_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231226304)))];
            tensor<fp32, [8, 128]> encoder_layers_9_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_9_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231230464)))];
            tensor<fp32, [8, 128]> encoder_layers_9_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_9_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231234624)))];
            tensor<fp32, [1024]> encoder_layers_9_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_9_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231238784)))];
            tensor<fp32, [1024, 1024]> encoder_layers_9_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231242944))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232292672))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232291584)))];
            tensor<fp32, [1024]> encoder_layers_9_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_9_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232296832)))];
            tensor<fp32, [1024, 1024]> encoder_layers_9_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232300992))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233350720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233349632)))];
            tensor<fp32, [1024]> encoder_layers_9_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_9_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233354880)))];
            tensor<fp32, [1024, 1024]> encoder_layers_9_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233359040))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234408768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234407680)))];
            tensor<fp32, [1024]> encoder_layers_9_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_9_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234412928)))];
            tensor<fp32, [1024, 1024]> encoder_layers_9_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234417088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235466816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235465728)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_9_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235470976)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_9_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235475136)))];
            tensor<fp32, [2048]> encoder_layers_9_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_9_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235479296)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_9_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235487552))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237586880))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237584768)))];
            tensor<fp32, [1024]> encoder_layers_9_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_9_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237595136)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_9_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237599296))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238649024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238647936)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_9_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238653184)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_9_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238657344)))];
            tensor<fp32, [4096]> encoder_layers_9_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_9_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238661504)))];
            tensor<fp32, [4096, 1024]> encoder_layers_9_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238677952))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242876480))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242872320)))];
            tensor<fp32, [1024]> encoder_layers_9_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_9_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242892928)))];
            tensor<fp32, [1024, 4096]> encoder_layers_9_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_9_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242897088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247092544))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247091456)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_9_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247096704)))];
            tensor<fp32, [1024]> encoder_layers_9_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_9_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247100864)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_10_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247105024)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_10_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247109184)))];
            tensor<fp32, [4096]> encoder_layers_10_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_10_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247113344)))];
            tensor<fp32, [4096, 1024]> encoder_layers_10_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247129792))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251328320))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251324160)))];
            tensor<fp32, [1024]> encoder_layers_10_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_10_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251344768)))];
            tensor<fp32, [1024, 4096]> encoder_layers_10_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251348928))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255544384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255543296)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_10_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255548544)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_10_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255552704)))];
            tensor<fp32, [8, 128]> encoder_layers_10_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_10_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255556864)))];
            tensor<fp32, [8, 128]> encoder_layers_10_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_10_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255561024)))];
            tensor<fp32, [1024]> encoder_layers_10_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_10_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255565184)))];
            tensor<fp32, [1024, 1024]> encoder_layers_10_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255569344))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256619072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256617984)))];
            tensor<fp32, [1024]> encoder_layers_10_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_10_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256623232)))];
            tensor<fp32, [1024, 1024]> encoder_layers_10_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256627392))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257677120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257676032)))];
            tensor<fp32, [1024]> encoder_layers_10_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_10_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257681280)))];
            tensor<fp32, [1024, 1024]> encoder_layers_10_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257685440))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258735168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258734080)))];
            tensor<fp32, [1024]> encoder_layers_10_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_10_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258739328)))];
            tensor<fp32, [1024, 1024]> encoder_layers_10_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258743488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259793216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259792128)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_10_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259797376)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_10_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259801536)))];
            tensor<fp32, [2048]> encoder_layers_10_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_10_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259805696)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_10_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259813952))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261913280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261911168)))];
            tensor<fp32, [1024]> encoder_layers_10_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_10_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261921536)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_10_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261925696))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262975424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262974336)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_10_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262979584)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_10_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262983744)))];
            tensor<fp32, [4096]> encoder_layers_10_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_10_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262987904)))];
            tensor<fp32, [4096, 1024]> encoder_layers_10_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263004352))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267202880))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267198720)))];
            tensor<fp32, [1024]> encoder_layers_10_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_10_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267219328)))];
            tensor<fp32, [1024, 4096]> encoder_layers_10_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_10_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267223488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271418944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271417856)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_10_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271423104)))];
            tensor<fp32, [1024]> encoder_layers_10_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_10_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271427264)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_11_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271431424)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_11_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271435584)))];
            tensor<fp32, [4096]> encoder_layers_11_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_11_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271439744)))];
            tensor<fp32, [4096, 1024]> encoder_layers_11_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271456192))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(275654720))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(275650560)))];
            tensor<fp32, [1024]> encoder_layers_11_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_11_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(275671168)))];
            tensor<fp32, [1024, 4096]> encoder_layers_11_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(275675328))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279870784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279869696)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_11_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279874944)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_11_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279879104)))];
            tensor<fp32, [8, 128]> encoder_layers_11_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_11_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279883264)))];
            tensor<fp32, [8, 128]> encoder_layers_11_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_11_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279887424)))];
            tensor<fp32, [1024]> encoder_layers_11_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_11_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279891584)))];
            tensor<fp32, [1024, 1024]> encoder_layers_11_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279895744))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(280945472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(280944384)))];
            tensor<fp32, [1024]> encoder_layers_11_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_11_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(280949632)))];
            tensor<fp32, [1024, 1024]> encoder_layers_11_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(280953792))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282003520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282002432)))];
            tensor<fp32, [1024]> encoder_layers_11_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_11_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282007680)))];
            tensor<fp32, [1024, 1024]> encoder_layers_11_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282011840))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283061568))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283060480)))];
            tensor<fp32, [1024]> encoder_layers_11_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_11_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283065728)))];
            tensor<fp32, [1024, 1024]> encoder_layers_11_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283069888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284119616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284118528)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_11_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284123776)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_11_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284127936)))];
            tensor<fp32, [2048]> encoder_layers_11_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_11_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284132096)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_11_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284140352))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286239680))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286237568)))];
            tensor<fp32, [1024]> encoder_layers_11_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_11_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286247936)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_11_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286252096))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287301824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287300736)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_11_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287305984)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_11_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287310144)))];
            tensor<fp32, [4096]> encoder_layers_11_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_11_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287314304)))];
            tensor<fp32, [4096, 1024]> encoder_layers_11_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287330752))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291529280))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291525120)))];
            tensor<fp32, [1024]> encoder_layers_11_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_11_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291545728)))];
            tensor<fp32, [1024, 4096]> encoder_layers_11_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_11_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291549888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295745344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295744256)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_11_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295749504)))];
            tensor<fp32, [1024]> encoder_layers_11_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_11_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295753664)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_12_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295757824)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_12_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295761984)))];
            tensor<fp32, [4096]> encoder_layers_12_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_12_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295766144)))];
            tensor<fp32, [4096, 1024]> encoder_layers_12_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295782592))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299981120))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299976960)))];
            tensor<fp32, [1024]> encoder_layers_12_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_12_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299997568)))];
            tensor<fp32, [1024, 4096]> encoder_layers_12_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300001728))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304197184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304196096)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_12_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304201344)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_12_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304205504)))];
            tensor<fp32, [8, 128]> encoder_layers_12_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_12_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304209664)))];
            tensor<fp32, [8, 128]> encoder_layers_12_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_12_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304213824)))];
            tensor<fp32, [1024]> encoder_layers_12_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_12_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304217984)))];
            tensor<fp32, [1024, 1024]> encoder_layers_12_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304222144))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305271872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305270784)))];
            tensor<fp32, [1024]> encoder_layers_12_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_12_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305276032)))];
            tensor<fp32, [1024, 1024]> encoder_layers_12_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305280192))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306329920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306328832)))];
            tensor<fp32, [1024]> encoder_layers_12_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_12_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306334080)))];
            tensor<fp32, [1024, 1024]> encoder_layers_12_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306338240))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307387968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307386880)))];
            tensor<fp32, [1024]> encoder_layers_12_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_12_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307392128)))];
            tensor<fp32, [1024, 1024]> encoder_layers_12_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307396288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308446016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308444928)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_12_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308450176)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_12_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308454336)))];
            tensor<fp32, [2048]> encoder_layers_12_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_12_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308458496)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_12_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308466752))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310566080))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310563968)))];
            tensor<fp32, [1024]> encoder_layers_12_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_12_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310574336)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_12_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310578496))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311628224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311627136)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_12_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311632384)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_12_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311636544)))];
            tensor<fp32, [4096]> encoder_layers_12_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_12_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311640704)))];
            tensor<fp32, [4096, 1024]> encoder_layers_12_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311657152))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315855680))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315851520)))];
            tensor<fp32, [1024]> encoder_layers_12_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_12_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315872128)))];
            tensor<fp32, [1024, 4096]> encoder_layers_12_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_12_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(315876288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320071744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320070656)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_12_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320075904)))];
            tensor<fp32, [1024]> encoder_layers_12_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_12_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320080064)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_13_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320084224)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_13_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320088384)))];
            tensor<fp32, [4096]> encoder_layers_13_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_13_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320092544)))];
            tensor<fp32, [4096, 1024]> encoder_layers_13_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320108992))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324307520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324303360)))];
            tensor<fp32, [1024]> encoder_layers_13_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_13_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324323968)))];
            tensor<fp32, [1024, 4096]> encoder_layers_13_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324328128))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328523584))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328522496)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_13_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328527744)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_13_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328531904)))];
            tensor<fp32, [8, 128]> encoder_layers_13_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_13_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328536064)))];
            tensor<fp32, [8, 128]> encoder_layers_13_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_13_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328540224)))];
            tensor<fp32, [1024]> encoder_layers_13_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_13_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328544384)))];
            tensor<fp32, [1024, 1024]> encoder_layers_13_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328548544))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(329598272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(329597184)))];
            tensor<fp32, [1024]> encoder_layers_13_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_13_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(329602432)))];
            tensor<fp32, [1024, 1024]> encoder_layers_13_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(329606592))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330656320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330655232)))];
            tensor<fp32, [1024]> encoder_layers_13_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_13_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330660480)))];
            tensor<fp32, [1024, 1024]> encoder_layers_13_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330664640))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331714368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331713280)))];
            tensor<fp32, [1024]> encoder_layers_13_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_13_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331718528)))];
            tensor<fp32, [1024, 1024]> encoder_layers_13_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331722688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332772416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332771328)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_13_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332776576)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_13_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332780736)))];
            tensor<fp32, [2048]> encoder_layers_13_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_13_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332784896)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_13_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332793152))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334892480))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334890368)))];
            tensor<fp32, [1024]> encoder_layers_13_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_13_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334900736)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_13_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334904896))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335954624))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335953536)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_13_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335958784)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_13_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335962944)))];
            tensor<fp32, [4096]> encoder_layers_13_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_13_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335967104)))];
            tensor<fp32, [4096, 1024]> encoder_layers_13_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335983552))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340182080))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340177920)))];
            tensor<fp32, [1024]> encoder_layers_13_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_13_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340198528)))];
            tensor<fp32, [1024, 4096]> encoder_layers_13_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_13_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340202688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344398144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344397056)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_13_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344402304)))];
            tensor<fp32, [1024]> encoder_layers_13_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_13_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344406464)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_14_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344410624)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_14_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344414784)))];
            tensor<fp32, [4096]> encoder_layers_14_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_14_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344418944)))];
            tensor<fp32, [4096, 1024]> encoder_layers_14_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344435392))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348633920))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348629760)))];
            tensor<fp32, [1024]> encoder_layers_14_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_14_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348650368)))];
            tensor<fp32, [1024, 4096]> encoder_layers_14_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348654528))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352849984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352848896)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_14_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352854144)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_14_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352858304)))];
            tensor<fp32, [8, 128]> encoder_layers_14_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_14_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352862464)))];
            tensor<fp32, [8, 128]> encoder_layers_14_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_14_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352866624)))];
            tensor<fp32, [1024]> encoder_layers_14_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_14_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352870784)))];
            tensor<fp32, [1024, 1024]> encoder_layers_14_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352874944))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353924672))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353923584)))];
            tensor<fp32, [1024]> encoder_layers_14_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_14_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353928832)))];
            tensor<fp32, [1024, 1024]> encoder_layers_14_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353932992))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354982720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354981632)))];
            tensor<fp32, [1024]> encoder_layers_14_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_14_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354986880)))];
            tensor<fp32, [1024, 1024]> encoder_layers_14_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354991040))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356040768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356039680)))];
            tensor<fp32, [1024]> encoder_layers_14_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_14_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356044928)))];
            tensor<fp32, [1024, 1024]> encoder_layers_14_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356049088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357098816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357097728)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_14_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357102976)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_14_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357107136)))];
            tensor<fp32, [2048]> encoder_layers_14_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_14_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357111296)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_14_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357119552))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359218880))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359216768)))];
            tensor<fp32, [1024]> encoder_layers_14_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_14_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359227136)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_14_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359231296))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360281024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360279936)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_14_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360285184)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_14_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360289344)))];
            tensor<fp32, [4096]> encoder_layers_14_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_14_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360293504)))];
            tensor<fp32, [4096, 1024]> encoder_layers_14_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360309952))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364508480))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364504320)))];
            tensor<fp32, [1024]> encoder_layers_14_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_14_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364524928)))];
            tensor<fp32, [1024, 4096]> encoder_layers_14_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_14_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364529088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368724544))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368723456)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_14_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368728704)))];
            tensor<fp32, [1024]> encoder_layers_14_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_14_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368732864)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_15_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368737024)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_15_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368741184)))];
            tensor<fp32, [4096]> encoder_layers_15_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_15_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368745344)))];
            tensor<fp32, [4096, 1024]> encoder_layers_15_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368761792))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372960320))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372956160)))];
            tensor<fp32, [1024]> encoder_layers_15_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_15_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372976768)))];
            tensor<fp32, [1024, 4096]> encoder_layers_15_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372980928))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377176384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377175296)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_15_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377180544)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_15_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377184704)))];
            tensor<fp32, [8, 128]> encoder_layers_15_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_15_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377188864)))];
            tensor<fp32, [8, 128]> encoder_layers_15_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_15_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377193024)))];
            tensor<fp32, [1024]> encoder_layers_15_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_15_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377197184)))];
            tensor<fp32, [1024, 1024]> encoder_layers_15_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377201344))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(378251072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(378249984)))];
            tensor<fp32, [1024]> encoder_layers_15_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_15_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(378255232)))];
            tensor<fp32, [1024, 1024]> encoder_layers_15_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(378259392))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379309120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379308032)))];
            tensor<fp32, [1024]> encoder_layers_15_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_15_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379313280)))];
            tensor<fp32, [1024, 1024]> encoder_layers_15_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379317440))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380367168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380366080)))];
            tensor<fp32, [1024]> encoder_layers_15_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_15_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380371328)))];
            tensor<fp32, [1024, 1024]> encoder_layers_15_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380375488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381425216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381424128)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_15_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381429376)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_15_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381433536)))];
            tensor<fp32, [2048]> encoder_layers_15_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_15_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381437696)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_15_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381445952))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383545280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383543168)))];
            tensor<fp32, [1024]> encoder_layers_15_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_15_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383553536)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_15_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383557696))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384607424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384606336)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_15_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384611584)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_15_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384615744)))];
            tensor<fp32, [4096]> encoder_layers_15_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_15_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384619904)))];
            tensor<fp32, [4096, 1024]> encoder_layers_15_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384636352))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388834880))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388830720)))];
            tensor<fp32, [1024]> encoder_layers_15_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_15_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388851328)))];
            tensor<fp32, [1024, 4096]> encoder_layers_15_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_15_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388855488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393050944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393049856)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_15_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393055104)))];
            tensor<fp32, [1024]> encoder_layers_15_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_15_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393059264)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_16_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393063424)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_16_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393067584)))];
            tensor<fp32, [4096]> encoder_layers_16_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_16_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393071744)))];
            tensor<fp32, [4096, 1024]> encoder_layers_16_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393088192))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397286720))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397282560)))];
            tensor<fp32, [1024]> encoder_layers_16_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_16_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397303168)))];
            tensor<fp32, [1024, 4096]> encoder_layers_16_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397307328))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401502784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401501696)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_16_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401506944)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_16_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401511104)))];
            tensor<fp32, [8, 128]> encoder_layers_16_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_16_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401515264)))];
            tensor<fp32, [8, 128]> encoder_layers_16_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_16_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401519424)))];
            tensor<fp32, [1024]> encoder_layers_16_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_16_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401523584)))];
            tensor<fp32, [1024, 1024]> encoder_layers_16_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401527744))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402577472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402576384)))];
            tensor<fp32, [1024]> encoder_layers_16_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_16_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402581632)))];
            tensor<fp32, [1024, 1024]> encoder_layers_16_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402585792))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403635520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403634432)))];
            tensor<fp32, [1024]> encoder_layers_16_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_16_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403639680)))];
            tensor<fp32, [1024, 1024]> encoder_layers_16_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403643840))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404693568))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404692480)))];
            tensor<fp32, [1024]> encoder_layers_16_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_16_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404697728)))];
            tensor<fp32, [1024, 1024]> encoder_layers_16_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404701888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405751616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405750528)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_16_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405755776)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_16_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405759936)))];
            tensor<fp32, [2048]> encoder_layers_16_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_16_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405764096)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_16_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405772352))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407871680))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407869568)))];
            tensor<fp32, [1024]> encoder_layers_16_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_16_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407879936)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_16_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407884096))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408933824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408932736)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_16_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408937984)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_16_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408942144)))];
            tensor<fp32, [4096]> encoder_layers_16_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_16_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408946304)))];
            tensor<fp32, [4096, 1024]> encoder_layers_16_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408962752))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413161280))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413157120)))];
            tensor<fp32, [1024]> encoder_layers_16_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_16_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413177728)))];
            tensor<fp32, [1024, 4096]> encoder_layers_16_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_16_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413181888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417377344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417376256)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_16_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417381504)))];
            tensor<fp32, [1024]> encoder_layers_16_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_16_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417385664)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_17_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417389824)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_17_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417393984)))];
            tensor<fp32, [4096]> encoder_layers_17_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_17_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417398144)))];
            tensor<fp32, [4096, 1024]> encoder_layers_17_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417414592))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421613120))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421608960)))];
            tensor<fp32, [1024]> encoder_layers_17_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_17_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421629568)))];
            tensor<fp32, [1024, 4096]> encoder_layers_17_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421633728))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425829184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425828096)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_17_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425833344)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_17_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425837504)))];
            tensor<fp32, [8, 128]> encoder_layers_17_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_17_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425841664)))];
            tensor<fp32, [8, 128]> encoder_layers_17_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_17_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425845824)))];
            tensor<fp32, [1024]> encoder_layers_17_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_17_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425849984)))];
            tensor<fp32, [1024, 1024]> encoder_layers_17_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425854144))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426903872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426902784)))];
            tensor<fp32, [1024]> encoder_layers_17_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_17_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426908032)))];
            tensor<fp32, [1024, 1024]> encoder_layers_17_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426912192))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427961920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427960832)))];
            tensor<fp32, [1024]> encoder_layers_17_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_17_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427966080)))];
            tensor<fp32, [1024, 1024]> encoder_layers_17_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427970240))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429019968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429018880)))];
            tensor<fp32, [1024]> encoder_layers_17_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_17_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429024128)))];
            tensor<fp32, [1024, 1024]> encoder_layers_17_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429028288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430078016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430076928)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_17_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430082176)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_17_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430086336)))];
            tensor<fp32, [2048]> encoder_layers_17_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_17_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430090496)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_17_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430098752))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432198080))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432195968)))];
            tensor<fp32, [1024]> encoder_layers_17_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_17_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432206336)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_17_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432210496))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433260224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433259136)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_17_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433264384)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_17_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433268544)))];
            tensor<fp32, [4096]> encoder_layers_17_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_17_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433272704)))];
            tensor<fp32, [4096, 1024]> encoder_layers_17_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433289152))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437487680))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437483520)))];
            tensor<fp32, [1024]> encoder_layers_17_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_17_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437504128)))];
            tensor<fp32, [1024, 4096]> encoder_layers_17_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_17_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437508288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441703744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441702656)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_17_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441707904)))];
            tensor<fp32, [1024]> encoder_layers_17_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_17_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441712064)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_18_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441716224)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_18_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441720384)))];
            tensor<fp32, [4096]> encoder_layers_18_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_18_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441724544)))];
            tensor<fp32, [4096, 1024]> encoder_layers_18_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441740992))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445939520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445935360)))];
            tensor<fp32, [1024]> encoder_layers_18_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_18_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445955968)))];
            tensor<fp32, [1024, 4096]> encoder_layers_18_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445960128))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450155584))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450154496)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_18_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450159744)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_18_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450163904)))];
            tensor<fp32, [8, 128]> encoder_layers_18_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_18_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450168064)))];
            tensor<fp32, [8, 128]> encoder_layers_18_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_18_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450172224)))];
            tensor<fp32, [1024]> encoder_layers_18_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_18_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450176384)))];
            tensor<fp32, [1024, 1024]> encoder_layers_18_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450180544))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451230272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451229184)))];
            tensor<fp32, [1024]> encoder_layers_18_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_18_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451234432)))];
            tensor<fp32, [1024, 1024]> encoder_layers_18_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451238592))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452288320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452287232)))];
            tensor<fp32, [1024]> encoder_layers_18_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_18_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452292480)))];
            tensor<fp32, [1024, 1024]> encoder_layers_18_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452296640))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453346368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453345280)))];
            tensor<fp32, [1024]> encoder_layers_18_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_18_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453350528)))];
            tensor<fp32, [1024, 1024]> encoder_layers_18_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453354688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454404416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454403328)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_18_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454408576)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_18_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454412736)))];
            tensor<fp32, [2048]> encoder_layers_18_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_18_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454416896)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_18_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454425152))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456524480))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456522368)))];
            tensor<fp32, [1024]> encoder_layers_18_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_18_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456532736)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_18_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456536896))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457586624))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457585536)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_18_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457590784)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_18_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457594944)))];
            tensor<fp32, [4096]> encoder_layers_18_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_18_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457599104)))];
            tensor<fp32, [4096, 1024]> encoder_layers_18_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457615552))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461814080))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461809920)))];
            tensor<fp32, [1024]> encoder_layers_18_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_18_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461830528)))];
            tensor<fp32, [1024, 4096]> encoder_layers_18_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_18_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461834688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466030144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466029056)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_18_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466034304)))];
            tensor<fp32, [1024]> encoder_layers_18_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_18_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466038464)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_19_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466042624)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_19_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466046784)))];
            tensor<fp32, [4096]> encoder_layers_19_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_19_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466050944)))];
            tensor<fp32, [4096, 1024]> encoder_layers_19_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466067392))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470265920))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470261760)))];
            tensor<fp32, [1024]> encoder_layers_19_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_19_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470282368)))];
            tensor<fp32, [1024, 4096]> encoder_layers_19_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470286528))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474481984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474480896)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_19_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474486144)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_19_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474490304)))];
            tensor<fp32, [8, 128]> encoder_layers_19_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_19_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474494464)))];
            tensor<fp32, [8, 128]> encoder_layers_19_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_19_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474498624)))];
            tensor<fp32, [1024]> encoder_layers_19_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_19_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474502784)))];
            tensor<fp32, [1024, 1024]> encoder_layers_19_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474506944))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475556672))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475555584)))];
            tensor<fp32, [1024]> encoder_layers_19_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_19_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475560832)))];
            tensor<fp32, [1024, 1024]> encoder_layers_19_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475564992))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476614720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476613632)))];
            tensor<fp32, [1024]> encoder_layers_19_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_19_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476618880)))];
            tensor<fp32, [1024, 1024]> encoder_layers_19_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476623040))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477672768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477671680)))];
            tensor<fp32, [1024]> encoder_layers_19_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_19_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477676928)))];
            tensor<fp32, [1024, 1024]> encoder_layers_19_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477681088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478730816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478729728)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_19_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478734976)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_19_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478739136)))];
            tensor<fp32, [2048]> encoder_layers_19_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_19_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478743296)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_19_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478751552))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480850880))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480848768)))];
            tensor<fp32, [1024]> encoder_layers_19_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_19_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480859136)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_19_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480863296))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481913024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481911936)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_19_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481917184)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_19_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481921344)))];
            tensor<fp32, [4096]> encoder_layers_19_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_19_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481925504)))];
            tensor<fp32, [4096, 1024]> encoder_layers_19_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481941952))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486140480))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486136320)))];
            tensor<fp32, [1024]> encoder_layers_19_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_19_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486156928)))];
            tensor<fp32, [1024, 4096]> encoder_layers_19_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_19_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486161088))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490356544))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490355456)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_19_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490360704)))];
            tensor<fp32, [1024]> encoder_layers_19_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_19_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490364864)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_20_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490369024)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_20_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490373184)))];
            tensor<fp32, [4096]> encoder_layers_20_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_20_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490377344)))];
            tensor<fp32, [4096, 1024]> encoder_layers_20_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490393792))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494592320))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494588160)))];
            tensor<fp32, [1024]> encoder_layers_20_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_20_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494608768)))];
            tensor<fp32, [1024, 4096]> encoder_layers_20_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494612928))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498808384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498807296)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_20_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498812544)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_20_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498816704)))];
            tensor<fp32, [8, 128]> encoder_layers_20_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_20_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498820864)))];
            tensor<fp32, [8, 128]> encoder_layers_20_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_20_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498825024)))];
            tensor<fp32, [1024]> encoder_layers_20_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_20_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498829184)))];
            tensor<fp32, [1024, 1024]> encoder_layers_20_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498833344))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499883072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499881984)))];
            tensor<fp32, [1024]> encoder_layers_20_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_20_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499887232)))];
            tensor<fp32, [1024, 1024]> encoder_layers_20_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499891392))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500941120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500940032)))];
            tensor<fp32, [1024]> encoder_layers_20_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_20_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500945280)))];
            tensor<fp32, [1024, 1024]> encoder_layers_20_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500949440))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501999168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501998080)))];
            tensor<fp32, [1024]> encoder_layers_20_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_20_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502003328)))];
            tensor<fp32, [1024, 1024]> encoder_layers_20_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502007488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503057216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503056128)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_20_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503061376)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_20_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503065536)))];
            tensor<fp32, [2048]> encoder_layers_20_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_20_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503069696)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_20_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503077952))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505177280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505175168)))];
            tensor<fp32, [1024]> encoder_layers_20_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_20_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505185536)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_20_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505189696))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506239424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506238336)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_20_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506243584)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_20_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506247744)))];
            tensor<fp32, [4096]> encoder_layers_20_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_20_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506251904)))];
            tensor<fp32, [4096, 1024]> encoder_layers_20_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506268352))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510466880))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510462720)))];
            tensor<fp32, [1024]> encoder_layers_20_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_20_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510483328)))];
            tensor<fp32, [1024, 4096]> encoder_layers_20_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_20_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510487488))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514682944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514681856)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_20_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514687104)))];
            tensor<fp32, [1024]> encoder_layers_20_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_20_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514691264)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_21_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514695424)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_21_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514699584)))];
            tensor<fp32, [4096]> encoder_layers_21_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_21_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514703744)))];
            tensor<fp32, [4096, 1024]> encoder_layers_21_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514720192))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(518918720))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(518914560)))];
            tensor<fp32, [1024]> encoder_layers_21_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_21_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(518935168)))];
            tensor<fp32, [1024, 4096]> encoder_layers_21_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(518939328))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523134784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523133696)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_21_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523138944)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_21_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523143104)))];
            tensor<fp32, [8, 128]> encoder_layers_21_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_21_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523147264)))];
            tensor<fp32, [8, 128]> encoder_layers_21_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_21_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523151424)))];
            tensor<fp32, [1024]> encoder_layers_21_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_21_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523155584)))];
            tensor<fp32, [1024, 1024]> encoder_layers_21_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523159744))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524209472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524208384)))];
            tensor<fp32, [1024]> encoder_layers_21_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_21_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524213632)))];
            tensor<fp32, [1024, 1024]> encoder_layers_21_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524217792))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525267520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525266432)))];
            tensor<fp32, [1024]> encoder_layers_21_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_21_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525271680)))];
            tensor<fp32, [1024, 1024]> encoder_layers_21_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525275840))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526325568))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526324480)))];
            tensor<fp32, [1024]> encoder_layers_21_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_21_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526329728)))];
            tensor<fp32, [1024, 1024]> encoder_layers_21_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526333888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527383616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527382528)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_21_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527387776)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_21_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527391936)))];
            tensor<fp32, [2048]> encoder_layers_21_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_21_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527396096)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_21_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527404352))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529503680))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529501568)))];
            tensor<fp32, [1024]> encoder_layers_21_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_21_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529511936)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_21_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529516096))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530565824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530564736)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_21_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530569984)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_21_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530574144)))];
            tensor<fp32, [4096]> encoder_layers_21_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_21_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530578304)))];
            tensor<fp32, [4096, 1024]> encoder_layers_21_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530594752))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534793280))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534789120)))];
            tensor<fp32, [1024]> encoder_layers_21_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_21_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534809728)))];
            tensor<fp32, [1024, 4096]> encoder_layers_21_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_21_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534813888))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539009344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539008256)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_21_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539013504)))];
            tensor<fp32, [1024]> encoder_layers_21_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_21_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539017664)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_22_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539021824)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_22_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539025984)))];
            tensor<fp32, [4096]> encoder_layers_22_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_22_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539030144)))];
            tensor<fp32, [4096, 1024]> encoder_layers_22_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539046592))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543245120))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543240960)))];
            tensor<fp32, [1024]> encoder_layers_22_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_22_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543261568)))];
            tensor<fp32, [1024, 4096]> encoder_layers_22_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543265728))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547461184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547460096)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_22_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547465344)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_22_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547469504)))];
            tensor<fp32, [8, 128]> encoder_layers_22_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_22_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547473664)))];
            tensor<fp32, [8, 128]> encoder_layers_22_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_22_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547477824)))];
            tensor<fp32, [1024]> encoder_layers_22_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_22_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547481984)))];
            tensor<fp32, [1024, 1024]> encoder_layers_22_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547486144))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548535872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548534784)))];
            tensor<fp32, [1024]> encoder_layers_22_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_22_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548540032)))];
            tensor<fp32, [1024, 1024]> encoder_layers_22_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548544192))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549593920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549592832)))];
            tensor<fp32, [1024]> encoder_layers_22_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_22_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549598080)))];
            tensor<fp32, [1024, 1024]> encoder_layers_22_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549602240))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550651968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550650880)))];
            tensor<fp32, [1024]> encoder_layers_22_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_22_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550656128)))];
            tensor<fp32, [1024, 1024]> encoder_layers_22_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550660288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551710016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551708928)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_22_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551714176)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_22_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551718336)))];
            tensor<fp32, [2048]> encoder_layers_22_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_22_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551722496)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_22_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551730752))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553830080))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553827968)))];
            tensor<fp32, [1024]> encoder_layers_22_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_22_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553838336)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_22_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553842496))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554892224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554891136)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_22_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554896384)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_22_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554900544)))];
            tensor<fp32, [4096]> encoder_layers_22_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_22_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554904704)))];
            tensor<fp32, [4096, 1024]> encoder_layers_22_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554921152))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559119680))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559115520)))];
            tensor<fp32, [1024]> encoder_layers_22_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_22_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559136128)))];
            tensor<fp32, [1024, 4096]> encoder_layers_22_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_22_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559140288))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563335744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563334656)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_22_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563339904)))];
            tensor<fp32, [1024]> encoder_layers_22_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_22_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563344064)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_feed_forward1_bias = const()[name = tensor<string, []>("encoder_layers_23_norm_feed_forward1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563348224)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_feed_forward1_weight = const()[name = tensor<string, []>("encoder_layers_23_norm_feed_forward1_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563352384)))];
            tensor<fp32, [4096]> encoder_layers_23_feed_forward1_linear1_bias = const()[name = tensor<string, []>("encoder_layers_23_feed_forward1_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563356544)))];
            tensor<fp32, [4096, 1024]> encoder_layers_23_feed_forward1_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_feed_forward1_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563372992))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567571520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567567360)))];
            tensor<fp32, [1024]> encoder_layers_23_feed_forward1_linear2_bias = const()[name = tensor<string, []>("encoder_layers_23_feed_forward1_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567587968)))];
            tensor<fp32, [1024, 4096]> encoder_layers_23_feed_forward1_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_feed_forward1_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567592128))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571787584))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571786496)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_self_att_bias = const()[name = tensor<string, []>("encoder_layers_23_norm_self_att_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571791744)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_self_att_weight = const()[name = tensor<string, []>("encoder_layers_23_norm_self_att_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571795904)))];
            tensor<fp32, [8, 128]> encoder_layers_23_self_attn_pos_bias_v = const()[name = tensor<string, []>("encoder_layers_23_self_attn_pos_bias_v"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571800064)))];
            tensor<fp32, [8, 128]> encoder_layers_23_self_attn_pos_bias_u = const()[name = tensor<string, []>("encoder_layers_23_self_attn_pos_bias_u"), val = tensor<fp32, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571804224)))];
            tensor<fp32, [1024]> encoder_layers_23_self_attn_linear_q_bias = const()[name = tensor<string, []>("encoder_layers_23_self_attn_linear_q_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571808384)))];
            tensor<fp32, [1024, 1024]> encoder_layers_23_self_attn_linear_q_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_self_attn_linear_q_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571812544))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572862272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572861184)))];
            tensor<fp32, [1024]> encoder_layers_23_self_attn_linear_k_bias = const()[name = tensor<string, []>("encoder_layers_23_self_attn_linear_k_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572866432)))];
            tensor<fp32, [1024, 1024]> encoder_layers_23_self_attn_linear_k_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_self_attn_linear_k_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572870592))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573920320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573919232)))];
            tensor<fp32, [1024]> encoder_layers_23_self_attn_linear_v_bias = const()[name = tensor<string, []>("encoder_layers_23_self_attn_linear_v_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573924480)))];
            tensor<fp32, [1024, 1024]> encoder_layers_23_self_attn_linear_v_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_self_attn_linear_v_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573928640))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574978368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574977280)))];
            tensor<fp32, [1024]> encoder_layers_23_self_attn_linear_out_bias = const()[name = tensor<string, []>("encoder_layers_23_self_attn_linear_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574982528)))];
            tensor<fp32, [1024, 1024]> encoder_layers_23_self_attn_linear_out_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_self_attn_linear_out_weight_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574986688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576036416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576035328)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_conv_bias = const()[name = tensor<string, []>("encoder_layers_23_norm_conv_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576040576)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_conv_weight = const()[name = tensor<string, []>("encoder_layers_23_norm_conv_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576044736)))];
            tensor<fp32, [2048]> encoder_layers_23_conv_pointwise_conv1_bias = const()[name = tensor<string, []>("encoder_layers_23_conv_pointwise_conv1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576048896)))];
            tensor<fp32, [2048, 1024, 1]> encoder_layers_23_conv_pointwise_conv1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_conv_pointwise_conv1_weight_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576057152))), scale = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578156480))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578154368)))];
            tensor<fp32, [1024]> encoder_layers_23_conv_pointwise_conv2_bias = const()[name = tensor<string, []>("encoder_layers_23_conv_pointwise_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578164736)))];
            tensor<fp32, [1024, 1024, 1]> encoder_layers_23_conv_pointwise_conv2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_conv_pointwise_conv2_weight_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578168896))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579218624))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579217536)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_feed_forward2_bias = const()[name = tensor<string, []>("encoder_layers_23_norm_feed_forward2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579222784)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_feed_forward2_weight = const()[name = tensor<string, []>("encoder_layers_23_norm_feed_forward2_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579226944)))];
            tensor<fp32, [4096]> encoder_layers_23_feed_forward2_linear1_bias = const()[name = tensor<string, []>("encoder_layers_23_feed_forward2_linear1_bias"), val = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579231104)))];
            tensor<fp32, [4096, 1024]> encoder_layers_23_feed_forward2_linear1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_feed_forward2_linear1_weight_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579247552))), scale = tensor<fp32, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583446080))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583441920)))];
            tensor<fp32, [1024]> encoder_layers_23_feed_forward2_linear2_bias = const()[name = tensor<string, []>("encoder_layers_23_feed_forward2_linear2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583462528)))];
            tensor<fp32, [1024, 4096]> encoder_layers_23_feed_forward2_linear2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_layers_23_feed_forward2_linear2_weight_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583466688))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587662144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587661056)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_out_bias = const()[name = tensor<string, []>("encoder_layers_23_norm_out_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587666304)))];
            tensor<fp32, [1024]> encoder_layers_23_norm_out_weight = const()[name = tensor<string, []>("encoder_layers_23_norm_out_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587670464)))];
            tensor<fp32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, []> var_13 = const()[name = tensor<string, []>("op_13"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, []> var_14 = const()[name = tensor<string, []>("op_14"), val = tensor<fp32, []>(-0x1.388p+13)];
            tensor<int32, []> var_32 = const()[name = tensor<string, []>("op_32"), val = tensor<int32, []>(-1)];
            tensor<int32, [3]> x_1_perm_0 = const()[name = tensor<string, []>("x_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> var_88_dtype_0 = const()[name = tensor<string, []>("op_88_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, []> var_89_promoted = const()[name = tensor<string, []>("op_89_promoted"), val = tensor<fp32, []>(-0x1p+0)];
            tensor<fp32, [1]> var_88 = cast(dtype = var_88_dtype_0, x = mel_length)[name = tensor<string, []>("cast_1")];
            tensor<fp32, [1]> var_90 = add(x = var_88, y = var_89_promoted)[name = tensor<string, []>("op_90")];
            tensor<fp32, []> _inversed_92_y_0 = const()[name = tensor<string, []>("_inversed_92_y_0"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1]> _inversed_92 = mul(x = var_90, y = _inversed_92_y_0)[name = tensor<string, []>("_inversed_92")];
            tensor<fp32, []> var_93 = const()[name = tensor<string, []>("op_93"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1]> lengths_1 = add(x = _inversed_92, y = var_93)[name = tensor<string, []>("lengths_1")];
            tensor<fp32, [1]> lengths_3 = floor(x = lengths_1)[name = tensor<string, []>("lengths_3")];
            tensor<fp32, []> var_97_promoted = const()[name = tensor<string, []>("op_97_promoted"), val = tensor<fp32, []>(-0x1p+0)];
            tensor<fp32, [1]> var_98 = add(x = lengths_3, y = var_97_promoted)[name = tensor<string, []>("op_98")];
            tensor<fp32, []> _inversed_100_y_0 = const()[name = tensor<string, []>("_inversed_100_y_0"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1]> _inversed_100 = mul(x = var_98, y = _inversed_100_y_0)[name = tensor<string, []>("_inversed_100")];
            tensor<fp32, []> var_101 = const()[name = tensor<string, []>("op_101"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1]> lengths_7 = add(x = _inversed_100, y = var_101)[name = tensor<string, []>("lengths_7")];
            tensor<fp32, [1]> lengths_9 = floor(x = lengths_7)[name = tensor<string, []>("lengths_9")];
            tensor<fp32, []> var_105_promoted = const()[name = tensor<string, []>("op_105_promoted"), val = tensor<fp32, []>(-0x1p+0)];
            tensor<fp32, [1]> var_106 = add(x = lengths_9, y = var_105_promoted)[name = tensor<string, []>("op_106")];
            tensor<fp32, []> _inversed_108_y_0 = const()[name = tensor<string, []>("_inversed_108_y_0"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1]> _inversed_108 = mul(x = var_106, y = _inversed_108_y_0)[name = tensor<string, []>("_inversed_108")];
            tensor<fp32, []> var_109 = const()[name = tensor<string, []>("op_109"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1]> lengths_13 = add(x = _inversed_108, y = var_109)[name = tensor<string, []>("lengths_13")];
            tensor<fp32, [1]> lengths = floor(x = lengths_13)[name = tensor<string, []>("lengths")];
            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1501, 80]> x_1 = transpose(perm = x_1_perm_0, x = melspectrogram_features)[name = tensor<string, []>("transpose_314")];
            tensor<fp32, [1, 1, 1501, 80]> input_1 = expand_dims(axes = input_1_axes_0, x = x_1)[name = tensor<string, []>("input_1")];
            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = tensor<string, []>("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_3_groups_0 = const()[name = tensor<string, []>("input_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 751, 40]> input_3 = conv(bias = encoder_pre_encode_conv_0_bias, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = encoder_pre_encode_conv_0_weight_quantized, x = input_1)[name = tensor<string, []>("input_3")];
            tensor<fp32, [1, 256, 751, 40]> input_5 = relu(x = input_3)[name = tensor<string, []>("input_5")];
            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp32, [1, 256, 376, 20]> input_7 = conv(bias = encoder_pre_encode_conv_2_bias, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = encoder_pre_encode_conv_2_weight_quantized, x = input_5)[name = tensor<string, []>("input_7")];
            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 376, 20]> input_9 = conv(bias = encoder_pre_encode_conv_3_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = encoder_pre_encode_conv_3_weight_quantized, x = input_7)[name = tensor<string, []>("input_9")];
            tensor<fp32, [1, 256, 376, 20]> input_11 = relu(x = input_9)[name = tensor<string, []>("input_11")];
            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp32, [1, 256, 188, 10]> input_13 = conv(bias = encoder_pre_encode_conv_5_bias, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = encoder_pre_encode_conv_5_weight_quantized, x = input_11)[name = tensor<string, []>("input_13")];
            tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = tensor<string, []>("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = tensor<string, []>("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_15_groups_0 = const()[name = tensor<string, []>("input_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 188, 10]> input_15 = conv(bias = encoder_pre_encode_conv_6_bias, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = encoder_pre_encode_conv_6_weight_quantized, x = input_13)[name = tensor<string, []>("input_15")];
            tensor<fp32, [1, 256, 188, 10]> x_3 = relu(x = input_15)[name = tensor<string, []>("x_3")];
            tensor<int32, [4]> var_159_perm_0 = const()[name = tensor<string, []>("op_159_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_160 = const()[name = tensor<string, []>("op_160"), val = tensor<int32, [3]>([1, 188, -1])];
            tensor<fp32, [1, 188, 256, 10]> var_159 = transpose(perm = var_159_perm_0, x = x_3)[name = tensor<string, []>("transpose_313")];
            tensor<fp32, [1, 188, 2560]> input_17 = reshape(shape = var_160, x = var_159)[name = tensor<string, []>("input_17")];
            tensor<fp32, [1, 188, 1024]> audio_signal_1 = linear(bias = encoder_pre_encode_out_bias, weight = encoder_pre_encode_out_weight_quantized, x = input_17)[name = tensor<string, []>("linear_0")];
            tensor<string, []> padding_length_dtype_0 = const()[name = tensor<string, []>("padding_length_dtype_0"), val = tensor<string, []>("int32")];
            tensor<fp32, []> var_171 = const()[name = tensor<string, []>("op_171"), val = tensor<fp32, []>(0x1p+5)];
            tensor<fp32, [1, 188, 1024]> x_5 = mul(x = audio_signal_1, y = var_171)[name = tensor<string, []>("x_5")];
            tensor<int32, [1, 188]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 188]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])];
            tensor<int32, [1]> var_200_axes_0 = const()[name = tensor<string, []>("op_200_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = lengths)[name = tensor<string, []>("cast_0")];
            tensor<int32, [1, 1]> var_200 = expand_dims(axes = var_200_axes_0, x = encoder_length)[name = tensor<string, []>("op_200")];
            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_0, y = var_200)[name = tensor<string, []>("pad_mask_1")];
            tensor<int32, [1]> var_202_axes_0 = const()[name = tensor<string, []>("op_202_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188]> var_202 = expand_dims(axes = var_202_axes_0, x = pad_mask_1)[name = tensor<string, []>("op_202")];
            tensor<int32, [3]> var_203 = const()[name = tensor<string, []>("op_203"), val = tensor<int32, [3]>([1, 188, 1])];
            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_203, x = var_202)[name = tensor<string, []>("pad_mask_for_att_mask_1")];
            tensor<int32, [3]> var_205_perm_0 = const()[name = tensor<string, []>("op_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, [1, 188, 188]> var_205 = transpose(perm = var_205_perm_0, x = pad_mask_for_att_mask_1)[name = tensor<string, []>("transpose_312")];
            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_205)[name = tensor<string, []>("pad_mask_for_att_mask")];
            tensor<bool, [1, 188, 188]> const_7 = const()[name = tensor<string, []>("const_7"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true, true, true]]])];
            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = tensor<string, []>("att_mask")];
            tensor<bool, [1, 188, 188]> mask_1 = logical_not(x = att_mask)[name = tensor<string, []>("mask_1")];
            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = tensor<string, []>("pad_mask")];
            tensor<int32, [1]> input_21_axes_0 = const()[name = tensor<string, []>("input_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_21 = layer_norm(axes = input_21_axes_0, beta = encoder_layers_0_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_0_norm_feed_forward1_weight, x = x_5)[name = tensor<string, []>("input_21")];
            tensor<fp32, [1, 188, 4096]> input_23 = linear(bias = encoder_layers_0_feed_forward1_linear1_bias, weight = encoder_layers_0_feed_forward1_linear1_weight_quantized, x = input_21)[name = tensor<string, []>("linear_1")];
            tensor<fp32, [1, 188, 4096]> input_25 = silu(x = input_23)[name = tensor<string, []>("input_25")];
            tensor<fp32, [1, 188, 1024]> input_29 = linear(bias = encoder_layers_0_feed_forward1_linear2_bias, weight = encoder_layers_0_feed_forward1_linear2_weight_quantized, x = input_25)[name = tensor<string, []>("linear_2")];
            tensor<fp32, []> var_238 = const()[name = tensor<string, []>("op_238"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_239 = mul(x = input_29, y = var_238)[name = tensor<string, []>("op_239")];
            tensor<fp32, [1, 188, 1024]> input_31 = add(x = x_5, y = var_239)[name = tensor<string, []>("input_31")];
            tensor<int32, [1]> query_1_axes_0 = const()[name = tensor<string, []>("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_1 = layer_norm(axes = query_1_axes_0, beta = encoder_layers_0_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_0_norm_self_att_weight, x = input_31)[name = tensor<string, []>("query_1")];
            tensor<fp32, [1, 188, 1024]> var_255 = linear(bias = encoder_layers_0_self_attn_linear_q_bias, weight = encoder_layers_0_self_attn_linear_q_weight_quantized, x = query_1)[name = tensor<string, []>("linear_3")];
            tensor<int32, [4]> var_256 = const()[name = tensor<string, []>("op_256"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_1 = reshape(shape = var_256, x = var_255)[name = tensor<string, []>("q_1")];
            tensor<fp32, [1, 188, 1024]> var_260 = linear(bias = encoder_layers_0_self_attn_linear_k_bias, weight = encoder_layers_0_self_attn_linear_k_weight_quantized, x = query_1)[name = tensor<string, []>("linear_4")];
            tensor<int32, [4]> var_261 = const()[name = tensor<string, []>("op_261"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_1 = reshape(shape = var_261, x = var_260)[name = tensor<string, []>("k_1")];
            tensor<fp32, [1, 188, 1024]> var_265 = linear(bias = encoder_layers_0_self_attn_linear_v_bias, weight = encoder_layers_0_self_attn_linear_v_weight_quantized, x = query_1)[name = tensor<string, []>("linear_5")];
            tensor<int32, [4]> var_266 = const()[name = tensor<string, []>("op_266"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_1 = reshape(shape = var_266, x = var_265)[name = tensor<string, []>("v_1")];
            tensor<int32, [4]> value_3_perm_0 = const()[name = tensor<string, []>("value_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_278 = add(x = q_1, y = encoder_layers_0_self_attn_pos_bias_u)[name = tensor<string, []>("op_278")];
            tensor<fp32, [1, 188, 8, 128]> var_280 = add(x = q_1, y = encoder_layers_0_self_attn_pos_bias_v)[name = tensor<string, []>("op_280")];
            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_282_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_282_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587674624))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588059136))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588058688)))];
            tensor<bool, []> x_9_transpose_x_0 = const()[name = tensor<string, []>("x_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_9_transpose_y_0 = const()[name = tensor<string, []>("x_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_1 = transpose(perm = q_with_bias_v_1_perm_0, x = var_280)[name = tensor<string, []>("transpose_311")];
            tensor<fp32, [1, 8, 188, 375]> x_9 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = q_with_bias_v_1, y = op_282_quantized)[name = tensor<string, []>("x_9")];
            tensor<fp32, []> const_14 = const()[name = tensor<string, []>("const_14"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_11_mode_0 = const()[name = tensor<string, []>("x_11_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_11 = pad(constant_val = const_14, mode = x_11_mode_0, pad = x_11_pad_0, x = x_9)[name = tensor<string, []>("x_11")];
            tensor<int32, [4]> var_290 = const()[name = tensor<string, []>("op_290"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_13 = reshape(shape = var_290, x = x_11)[name = tensor<string, []>("x_13")];
            tensor<int32, [4]> var_294_begin_0 = const()[name = tensor<string, []>("op_294_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_294_end_0 = const()[name = tensor<string, []>("op_294_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_294_end_mask_0 = const()[name = tensor<string, []>("op_294_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_294 = slice_by_index(begin = var_294_begin_0, end = var_294_end_0, end_mask = var_294_end_mask_0, x = x_13)[name = tensor<string, []>("op_294")];
            tensor<int32, [4]> var_295 = const()[name = tensor<string, []>("op_295"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_1 = reshape(shape = var_295, x = var_294)[name = tensor<string, []>("matrix_bd_1")];
            tensor<bool, []> matrix_ac_1_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_1_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_96_perm_0 = const()[name = tensor<string, []>("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_97_perm_0 = const()[name = tensor<string, []>("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1)[name = tensor<string, []>("transpose_309")];
            tensor<fp32, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_278)[name = tensor<string, []>("transpose_310")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_1 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = tensor<string, []>("matrix_ac_1")];
            tensor<int32, [4]> matrix_bd_3_begin_0 = const()[name = tensor<string, []>("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = tensor<string, []>("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_3 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1)[name = tensor<string, []>("matrix_bd_3")];
            tensor<fp32, [1, 8, 188, 188]> var_304 = add(x = matrix_ac_1, y = matrix_bd_3)[name = tensor<string, []>("op_304")];
            tensor<fp32, []> _inversed_scores_1_y_0 = const()[name = tensor<string, []>("_inversed_scores_1_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_1 = mul(x = var_304, y = _inversed_scores_1_y_0)[name = tensor<string, []>("_inversed_scores_1")];
            tensor<int32, [1]> mask_3_axes_0 = const()[name = tensor<string, []>("mask_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188, 188]> mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = tensor<string, []>("mask_3")];
            tensor<fp32, [1, 8, 188, 188]> scores_3 = select(a = var_14, b = _inversed_scores_1, cond = mask_3)[name = tensor<string, []>("scores_3")];
            tensor<fp32, [1, 8, 188, 188]> var_310 = softmax(axis = var_32, x = scores_3)[name = tensor<string, []>("op_310")];
            tensor<fp32, [1, 8, 188, 188]> input_33 = select(a = var_13, b = var_310, cond = mask_3)[name = tensor<string, []>("input_33")];
            tensor<bool, []> x_15_transpose_x_0 = const()[name = tensor<string, []>("x_15_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_15_transpose_y_0 = const()[name = tensor<string, []>("x_15_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_3 = transpose(perm = value_3_perm_0, x = v_1)[name = tensor<string, []>("transpose_308")];
            tensor<fp32, [1, 8, 188, 128]> x_15 = matmul(transpose_x = x_15_transpose_x_0, transpose_y = x_15_transpose_y_0, x = input_33, y = value_3)[name = tensor<string, []>("x_15")];
            tensor<int32, [4]> var_314_perm_0 = const()[name = tensor<string, []>("op_314_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_315 = const()[name = tensor<string, []>("op_315"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_314 = transpose(perm = var_314_perm_0, x = x_15)[name = tensor<string, []>("transpose_307")];
            tensor<fp32, [1, 188, 1024]> input_35 = reshape(shape = var_315, x = var_314)[name = tensor<string, []>("input_35")];
            tensor<fp32, [1, 188, 1024]> input_37 = linear(bias = encoder_layers_0_self_attn_linear_out_bias, weight = encoder_layers_0_self_attn_linear_out_weight_quantized, x = input_35)[name = tensor<string, []>("linear_7")];
            tensor<fp32, [1, 188, 1024]> input_39 = add(x = input_31, y = input_37)[name = tensor<string, []>("input_39")];
            tensor<int32, [1]> x_19_axes_0 = const()[name = tensor<string, []>("x_19_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_19 = layer_norm(axes = x_19_axes_0, beta = encoder_layers_0_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_0_norm_conv_weight, x = input_39)[name = tensor<string, []>("x_19")];
            tensor<int32, [3]> input_41_perm_0 = const()[name = tensor<string, []>("input_41_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_41 = transpose(perm = input_41_perm_0, x = x_19)[name = tensor<string, []>("transpose_306")];
            tensor<fp32, [1, 2048, 188]> input_43 = conv(bias = encoder_layers_0_conv_pointwise_conv1_bias, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = encoder_layers_0_conv_pointwise_conv1_weight_quantized, x = input_41)[name = tensor<string, []>("input_43")];
            tensor<int32, []> x_21_split_num_splits_0 = const()[name = tensor<string, []>("x_21_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_21_split_axis_0 = const()[name = tensor<string, []>("x_21_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_21_split_0, tensor<fp32, [1, 1024, 188]> x_21_split_1 = split(axis = x_21_split_axis_0, num_splits = x_21_split_num_splits_0, x = input_43)[name = tensor<string, []>("x_21_split")];
            tensor<fp32, [1, 1024, 188]> x_21_split_1_sigmoid = sigmoid(x = x_21_split_1)[name = tensor<string, []>("x_21_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_21 = mul(x = x_21_split_0, y = x_21_split_1_sigmoid)[name = tensor<string, []>("x_21")];
            tensor<int32, [1]> var_339_axes_0 = const()[name = tensor<string, []>("op_339_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188]> var_339 = expand_dims(axes = var_339_axes_0, x = pad_mask)[name = tensor<string, []>("op_339")];
            tensor<fp32, [1, 1024, 188]> input_45 = select(a = var_13, b = x_21, cond = var_339)[name = tensor<string, []>("input_45")];
            tensor<fp32, []> const_17 = const()[name = tensor<string, []>("const_17"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_47_pad_0 = const()[name = tensor<string, []>("input_47_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_47_mode_0 = const()[name = tensor<string, []>("input_47_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_47 = pad(constant_val = const_17, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45)[name = tensor<string, []>("input_47")];
            tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_49_groups_0 = const()[name = tensor<string, []>("input_49_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_49_strides_0 = const()[name = tensor<string, []>("input_49_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_49_dilations_0 = const()[name = tensor<string, []>("input_49_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_248_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_248_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588060736))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588071104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588070016)))];
            tensor<fp32, [1024]> const_249 = const()[name = tensor<string, []>("const_249"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588075264)))];
            tensor<fp32, [1, 1024, 188]> input_51 = conv(bias = const_249, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_quantized, x = input_47)[name = tensor<string, []>("input_51")];
            tensor<fp32, [1, 1024, 188]> input_53 = silu(x = input_51)[name = tensor<string, []>("input_53")];
            tensor<string, []> x_23_pad_type_0 = const()[name = tensor<string, []>("x_23_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_23_strides_0 = const()[name = tensor<string, []>("x_23_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_23_pad_0 = const()[name = tensor<string, []>("x_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_23_dilations_0 = const()[name = tensor<string, []>("x_23_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_23_groups_0 = const()[name = tensor<string, []>("x_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_23 = conv(bias = encoder_layers_0_conv_pointwise_conv2_bias, dilations = x_23_dilations_0, groups = x_23_groups_0, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = x_23_strides_0, weight = encoder_layers_0_conv_pointwise_conv2_weight_quantized, x = input_53)[name = tensor<string, []>("x_23")];
            tensor<int32, [3]> input_55_perm_0 = const()[name = tensor<string, []>("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_55 = transpose(perm = input_55_perm_0, x = x_23)[name = tensor<string, []>("transpose_305")];
            tensor<fp32, [1, 188, 1024]> input_57 = add(x = input_39, y = input_55)[name = tensor<string, []>("input_57")];
            tensor<int32, [1]> input_59_axes_0 = const()[name = tensor<string, []>("input_59_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_59 = layer_norm(axes = input_59_axes_0, beta = encoder_layers_0_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_0_norm_feed_forward2_weight, x = input_57)[name = tensor<string, []>("input_59")];
            tensor<fp32, [1, 188, 4096]> input_61 = linear(bias = encoder_layers_0_feed_forward2_linear1_bias, weight = encoder_layers_0_feed_forward2_linear1_weight_quantized, x = input_59)[name = tensor<string, []>("linear_8")];
            tensor<fp32, [1, 188, 4096]> input_63 = silu(x = input_61)[name = tensor<string, []>("input_63")];
            tensor<fp32, [1, 188, 1024]> input_67 = linear(bias = encoder_layers_0_feed_forward2_linear2_bias, weight = encoder_layers_0_feed_forward2_linear2_weight_quantized, x = input_63)[name = tensor<string, []>("linear_9")];
            tensor<fp32, []> var_381 = const()[name = tensor<string, []>("op_381"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_382 = mul(x = input_67, y = var_381)[name = tensor<string, []>("op_382")];
            tensor<fp32, [1, 188, 1024]> input_69 = add(x = input_57, y = var_382)[name = tensor<string, []>("input_69")];
            tensor<int32, [1]> input_71_axes_0 = const()[name = tensor<string, []>("input_71_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_71 = layer_norm(axes = input_71_axes_0, beta = encoder_layers_0_norm_out_bias, epsilon = var_11, gamma = encoder_layers_0_norm_out_weight, x = input_69)[name = tensor<string, []>("input_71")];
            tensor<int32, [1]> input_73_axes_0 = const()[name = tensor<string, []>("input_73_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_73 = layer_norm(axes = input_73_axes_0, beta = encoder_layers_1_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_1_norm_feed_forward1_weight, x = input_71)[name = tensor<string, []>("input_73")];
            tensor<fp32, [1, 188, 4096]> input_75 = linear(bias = encoder_layers_1_feed_forward1_linear1_bias, weight = encoder_layers_1_feed_forward1_linear1_weight_quantized, x = input_73)[name = tensor<string, []>("linear_10")];
            tensor<fp32, [1, 188, 4096]> input_77 = silu(x = input_75)[name = tensor<string, []>("input_77")];
            tensor<fp32, [1, 188, 1024]> input_81 = linear(bias = encoder_layers_1_feed_forward1_linear2_bias, weight = encoder_layers_1_feed_forward1_linear2_weight_quantized, x = input_77)[name = tensor<string, []>("linear_11")];
            tensor<fp32, []> var_412 = const()[name = tensor<string, []>("op_412"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_413 = mul(x = input_81, y = var_412)[name = tensor<string, []>("op_413")];
            tensor<fp32, [1, 188, 1024]> input_83 = add(x = input_71, y = var_413)[name = tensor<string, []>("input_83")];
            tensor<int32, [1]> query_3_axes_0 = const()[name = tensor<string, []>("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_3 = layer_norm(axes = query_3_axes_0, beta = encoder_layers_1_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_1_norm_self_att_weight, x = input_83)[name = tensor<string, []>("query_3")];
            tensor<fp32, [1, 188, 1024]> var_429 = linear(bias = encoder_layers_1_self_attn_linear_q_bias, weight = encoder_layers_1_self_attn_linear_q_weight_quantized, x = query_3)[name = tensor<string, []>("linear_12")];
            tensor<int32, [4]> var_430 = const()[name = tensor<string, []>("op_430"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_7 = reshape(shape = var_430, x = var_429)[name = tensor<string, []>("q_7")];
            tensor<fp32, [1, 188, 1024]> var_434 = linear(bias = encoder_layers_1_self_attn_linear_k_bias, weight = encoder_layers_1_self_attn_linear_k_weight_quantized, x = query_3)[name = tensor<string, []>("linear_13")];
            tensor<int32, [4]> var_435 = const()[name = tensor<string, []>("op_435"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_5 = reshape(shape = var_435, x = var_434)[name = tensor<string, []>("k_5")];
            tensor<fp32, [1, 188, 1024]> var_439 = linear(bias = encoder_layers_1_self_attn_linear_v_bias, weight = encoder_layers_1_self_attn_linear_v_weight_quantized, x = query_3)[name = tensor<string, []>("linear_14")];
            tensor<int32, [4]> var_440 = const()[name = tensor<string, []>("op_440"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_3 = reshape(shape = var_440, x = var_439)[name = tensor<string, []>("v_3")];
            tensor<int32, [4]> value_5_perm_0 = const()[name = tensor<string, []>("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_452 = add(x = q_7, y = encoder_layers_1_self_attn_pos_bias_u)[name = tensor<string, []>("op_452")];
            tensor<fp32, [1, 188, 8, 128]> var_454 = add(x = q_7, y = encoder_layers_1_self_attn_pos_bias_v)[name = tensor<string, []>("op_454")];
            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_456_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_456_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588079424))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588463936))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588463488)))];
            tensor<bool, []> x_31_transpose_x_0 = const()[name = tensor<string, []>("x_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_31_transpose_y_0 = const()[name = tensor<string, []>("x_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_3 = transpose(perm = q_with_bias_v_3_perm_0, x = var_454)[name = tensor<string, []>("transpose_304")];
            tensor<fp32, [1, 8, 188, 375]> x_31 = matmul(transpose_x = x_31_transpose_x_0, transpose_y = x_31_transpose_y_0, x = q_with_bias_v_3, y = op_456_quantized)[name = tensor<string, []>("x_31")];
            tensor<fp32, []> const_24 = const()[name = tensor<string, []>("const_24"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_33_pad_0 = const()[name = tensor<string, []>("x_33_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_33_mode_0 = const()[name = tensor<string, []>("x_33_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_33 = pad(constant_val = const_24, mode = x_33_mode_0, pad = x_33_pad_0, x = x_31)[name = tensor<string, []>("x_33")];
            tensor<int32, [4]> var_464 = const()[name = tensor<string, []>("op_464"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_35 = reshape(shape = var_464, x = x_33)[name = tensor<string, []>("x_35")];
            tensor<int32, [4]> var_468_begin_0 = const()[name = tensor<string, []>("op_468_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_468_end_0 = const()[name = tensor<string, []>("op_468_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_468_end_mask_0 = const()[name = tensor<string, []>("op_468_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_468 = slice_by_index(begin = var_468_begin_0, end = var_468_end_0, end_mask = var_468_end_mask_0, x = x_35)[name = tensor<string, []>("op_468")];
            tensor<int32, [4]> var_469 = const()[name = tensor<string, []>("op_469"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_5 = reshape(shape = var_469, x = var_468)[name = tensor<string, []>("matrix_bd_5")];
            tensor<bool, []> matrix_ac_3_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_3_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_98_perm_0 = const()[name = tensor<string, []>("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_99_perm_0 = const()[name = tensor<string, []>("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5)[name = tensor<string, []>("transpose_302")];
            tensor<fp32, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_452)[name = tensor<string, []>("transpose_303")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_3 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = tensor<string, []>("matrix_ac_3")];
            tensor<int32, [4]> matrix_bd_7_begin_0 = const()[name = tensor<string, []>("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = tensor<string, []>("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_7 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5)[name = tensor<string, []>("matrix_bd_7")];
            tensor<fp32, [1, 8, 188, 188]> var_478 = add(x = matrix_ac_3, y = matrix_bd_7)[name = tensor<string, []>("op_478")];
            tensor<fp32, []> _inversed_scores_5_y_0 = const()[name = tensor<string, []>("_inversed_scores_5_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_5 = mul(x = var_478, y = _inversed_scores_5_y_0)[name = tensor<string, []>("_inversed_scores_5")];
            tensor<fp32, [1, 8, 188, 188]> scores_7 = select(a = var_14, b = _inversed_scores_5, cond = mask_3)[name = tensor<string, []>("scores_7")];
            tensor<fp32, [1, 8, 188, 188]> var_484 = softmax(axis = var_32, x = scores_7)[name = tensor<string, []>("op_484")];
            tensor<fp32, [1, 8, 188, 188]> input_85 = select(a = var_13, b = var_484, cond = mask_3)[name = tensor<string, []>("input_85")];
            tensor<bool, []> x_37_transpose_x_0 = const()[name = tensor<string, []>("x_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_37_transpose_y_0 = const()[name = tensor<string, []>("x_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_5 = transpose(perm = value_5_perm_0, x = v_3)[name = tensor<string, []>("transpose_301")];
            tensor<fp32, [1, 8, 188, 128]> x_37 = matmul(transpose_x = x_37_transpose_x_0, transpose_y = x_37_transpose_y_0, x = input_85, y = value_5)[name = tensor<string, []>("x_37")];
            tensor<int32, [4]> var_488_perm_0 = const()[name = tensor<string, []>("op_488_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_489 = const()[name = tensor<string, []>("op_489"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_488 = transpose(perm = var_488_perm_0, x = x_37)[name = tensor<string, []>("transpose_300")];
            tensor<fp32, [1, 188, 1024]> input_87 = reshape(shape = var_489, x = var_488)[name = tensor<string, []>("input_87")];
            tensor<fp32, [1, 188, 1024]> input_89 = linear(bias = encoder_layers_1_self_attn_linear_out_bias, weight = encoder_layers_1_self_attn_linear_out_weight_quantized, x = input_87)[name = tensor<string, []>("linear_16")];
            tensor<fp32, [1, 188, 1024]> input_91 = add(x = input_83, y = input_89)[name = tensor<string, []>("input_91")];
            tensor<int32, [1]> x_41_axes_0 = const()[name = tensor<string, []>("x_41_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_41 = layer_norm(axes = x_41_axes_0, beta = encoder_layers_1_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_1_norm_conv_weight, x = input_91)[name = tensor<string, []>("x_41")];
            tensor<int32, [3]> input_93_perm_0 = const()[name = tensor<string, []>("input_93_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_95_pad_type_0 = const()[name = tensor<string, []>("input_95_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_95_strides_0 = const()[name = tensor<string, []>("input_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_95_pad_0 = const()[name = tensor<string, []>("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_95_dilations_0 = const()[name = tensor<string, []>("input_95_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_95_groups_0 = const()[name = tensor<string, []>("input_95_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_93 = transpose(perm = input_93_perm_0, x = x_41)[name = tensor<string, []>("transpose_299")];
            tensor<fp32, [1, 2048, 188]> input_95 = conv(bias = encoder_layers_1_conv_pointwise_conv1_bias, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = encoder_layers_1_conv_pointwise_conv1_weight_quantized, x = input_93)[name = tensor<string, []>("input_95")];
            tensor<int32, []> x_43_split_num_splits_0 = const()[name = tensor<string, []>("x_43_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_43_split_axis_0 = const()[name = tensor<string, []>("x_43_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_43_split_0, tensor<fp32, [1, 1024, 188]> x_43_split_1 = split(axis = x_43_split_axis_0, num_splits = x_43_split_num_splits_0, x = input_95)[name = tensor<string, []>("x_43_split")];
            tensor<fp32, [1, 1024, 188]> x_43_split_1_sigmoid = sigmoid(x = x_43_split_1)[name = tensor<string, []>("x_43_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_43 = mul(x = x_43_split_0, y = x_43_split_1_sigmoid)[name = tensor<string, []>("x_43")];
            tensor<fp32, [1, 1024, 188]> input_97 = select(a = var_13, b = x_43, cond = var_339)[name = tensor<string, []>("input_97")];
            tensor<fp32, []> const_27 = const()[name = tensor<string, []>("const_27"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_99_mode_0 = const()[name = tensor<string, []>("input_99_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_99 = pad(constant_val = const_27, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97)[name = tensor<string, []>("input_99")];
            tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_250_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_250_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588465536))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588475904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588474816)))];
            tensor<fp32, [1024]> const_251 = const()[name = tensor<string, []>("const_251"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588480064)))];
            tensor<fp32, [1, 1024, 188]> input_103 = conv(bias = const_251, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_quantized, x = input_99)[name = tensor<string, []>("input_103")];
            tensor<fp32, [1, 1024, 188]> input_105 = silu(x = input_103)[name = tensor<string, []>("input_105")];
            tensor<string, []> x_45_pad_type_0 = const()[name = tensor<string, []>("x_45_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_45_strides_0 = const()[name = tensor<string, []>("x_45_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_45_pad_0 = const()[name = tensor<string, []>("x_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_45_dilations_0 = const()[name = tensor<string, []>("x_45_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_45_groups_0 = const()[name = tensor<string, []>("x_45_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_45 = conv(bias = encoder_layers_1_conv_pointwise_conv2_bias, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = encoder_layers_1_conv_pointwise_conv2_weight_quantized, x = input_105)[name = tensor<string, []>("x_45")];
            tensor<int32, [3]> input_107_perm_0 = const()[name = tensor<string, []>("input_107_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_107 = transpose(perm = input_107_perm_0, x = x_45)[name = tensor<string, []>("transpose_298")];
            tensor<fp32, [1, 188, 1024]> input_109 = add(x = input_91, y = input_107)[name = tensor<string, []>("input_109")];
            tensor<int32, [1]> input_111_axes_0 = const()[name = tensor<string, []>("input_111_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_111 = layer_norm(axes = input_111_axes_0, beta = encoder_layers_1_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_1_norm_feed_forward2_weight, x = input_109)[name = tensor<string, []>("input_111")];
            tensor<fp32, [1, 188, 4096]> input_113 = linear(bias = encoder_layers_1_feed_forward2_linear1_bias, weight = encoder_layers_1_feed_forward2_linear1_weight_quantized, x = input_111)[name = tensor<string, []>("linear_17")];
            tensor<fp32, [1, 188, 4096]> input_115 = silu(x = input_113)[name = tensor<string, []>("input_115")];
            tensor<fp32, [1, 188, 1024]> input_119 = linear(bias = encoder_layers_1_feed_forward2_linear2_bias, weight = encoder_layers_1_feed_forward2_linear2_weight_quantized, x = input_115)[name = tensor<string, []>("linear_18")];
            tensor<fp32, []> var_555 = const()[name = tensor<string, []>("op_555"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_556 = mul(x = input_119, y = var_555)[name = tensor<string, []>("op_556")];
            tensor<fp32, [1, 188, 1024]> input_121 = add(x = input_109, y = var_556)[name = tensor<string, []>("input_121")];
            tensor<int32, [1]> input_123_axes_0 = const()[name = tensor<string, []>("input_123_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_123 = layer_norm(axes = input_123_axes_0, beta = encoder_layers_1_norm_out_bias, epsilon = var_11, gamma = encoder_layers_1_norm_out_weight, x = input_121)[name = tensor<string, []>("input_123")];
            tensor<int32, [1]> input_125_axes_0 = const()[name = tensor<string, []>("input_125_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_125 = layer_norm(axes = input_125_axes_0, beta = encoder_layers_2_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_2_norm_feed_forward1_weight, x = input_123)[name = tensor<string, []>("input_125")];
            tensor<fp32, [1, 188, 4096]> input_127 = linear(bias = encoder_layers_2_feed_forward1_linear1_bias, weight = encoder_layers_2_feed_forward1_linear1_weight_quantized, x = input_125)[name = tensor<string, []>("linear_19")];
            tensor<fp32, [1, 188, 4096]> input_129 = silu(x = input_127)[name = tensor<string, []>("input_129")];
            tensor<fp32, [1, 188, 1024]> input_133 = linear(bias = encoder_layers_2_feed_forward1_linear2_bias, weight = encoder_layers_2_feed_forward1_linear2_weight_quantized, x = input_129)[name = tensor<string, []>("linear_20")];
            tensor<fp32, []> var_586 = const()[name = tensor<string, []>("op_586"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_587 = mul(x = input_133, y = var_586)[name = tensor<string, []>("op_587")];
            tensor<fp32, [1, 188, 1024]> input_135 = add(x = input_123, y = var_587)[name = tensor<string, []>("input_135")];
            tensor<int32, [1]> query_5_axes_0 = const()[name = tensor<string, []>("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_5 = layer_norm(axes = query_5_axes_0, beta = encoder_layers_2_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_2_norm_self_att_weight, x = input_135)[name = tensor<string, []>("query_5")];
            tensor<fp32, [1, 188, 1024]> var_603 = linear(bias = encoder_layers_2_self_attn_linear_q_bias, weight = encoder_layers_2_self_attn_linear_q_weight_quantized, x = query_5)[name = tensor<string, []>("linear_21")];
            tensor<int32, [4]> var_604 = const()[name = tensor<string, []>("op_604"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_13 = reshape(shape = var_604, x = var_603)[name = tensor<string, []>("q_13")];
            tensor<fp32, [1, 188, 1024]> var_608 = linear(bias = encoder_layers_2_self_attn_linear_k_bias, weight = encoder_layers_2_self_attn_linear_k_weight_quantized, x = query_5)[name = tensor<string, []>("linear_22")];
            tensor<int32, [4]> var_609 = const()[name = tensor<string, []>("op_609"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_9 = reshape(shape = var_609, x = var_608)[name = tensor<string, []>("k_9")];
            tensor<fp32, [1, 188, 1024]> var_613 = linear(bias = encoder_layers_2_self_attn_linear_v_bias, weight = encoder_layers_2_self_attn_linear_v_weight_quantized, x = query_5)[name = tensor<string, []>("linear_23")];
            tensor<int32, [4]> var_614 = const()[name = tensor<string, []>("op_614"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_5 = reshape(shape = var_614, x = var_613)[name = tensor<string, []>("v_5")];
            tensor<int32, [4]> value_7_perm_0 = const()[name = tensor<string, []>("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_626 = add(x = q_13, y = encoder_layers_2_self_attn_pos_bias_u)[name = tensor<string, []>("op_626")];
            tensor<fp32, [1, 188, 8, 128]> var_628 = add(x = q_13, y = encoder_layers_2_self_attn_pos_bias_v)[name = tensor<string, []>("op_628")];
            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_630_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_630_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588484224))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588868736))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588868288)))];
            tensor<bool, []> x_53_transpose_x_0 = const()[name = tensor<string, []>("x_53_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_53_transpose_y_0 = const()[name = tensor<string, []>("x_53_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_5 = transpose(perm = q_with_bias_v_5_perm_0, x = var_628)[name = tensor<string, []>("transpose_297")];
            tensor<fp32, [1, 8, 188, 375]> x_53 = matmul(transpose_x = x_53_transpose_x_0, transpose_y = x_53_transpose_y_0, x = q_with_bias_v_5, y = op_630_quantized)[name = tensor<string, []>("x_53")];
            tensor<fp32, []> const_34 = const()[name = tensor<string, []>("const_34"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_55_pad_0 = const()[name = tensor<string, []>("x_55_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_55_mode_0 = const()[name = tensor<string, []>("x_55_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_55 = pad(constant_val = const_34, mode = x_55_mode_0, pad = x_55_pad_0, x = x_53)[name = tensor<string, []>("x_55")];
            tensor<int32, [4]> var_638 = const()[name = tensor<string, []>("op_638"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_57 = reshape(shape = var_638, x = x_55)[name = tensor<string, []>("x_57")];
            tensor<int32, [4]> var_642_begin_0 = const()[name = tensor<string, []>("op_642_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_642_end_0 = const()[name = tensor<string, []>("op_642_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_642_end_mask_0 = const()[name = tensor<string, []>("op_642_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_642 = slice_by_index(begin = var_642_begin_0, end = var_642_end_0, end_mask = var_642_end_mask_0, x = x_57)[name = tensor<string, []>("op_642")];
            tensor<int32, [4]> var_643 = const()[name = tensor<string, []>("op_643"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_9 = reshape(shape = var_643, x = var_642)[name = tensor<string, []>("matrix_bd_9")];
            tensor<bool, []> matrix_ac_5_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_5_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_100_perm_0 = const()[name = tensor<string, []>("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_101_perm_0 = const()[name = tensor<string, []>("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9)[name = tensor<string, []>("transpose_295")];
            tensor<fp32, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_626)[name = tensor<string, []>("transpose_296")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_5 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = tensor<string, []>("matrix_ac_5")];
            tensor<int32, [4]> matrix_bd_11_begin_0 = const()[name = tensor<string, []>("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = tensor<string, []>("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_11 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9)[name = tensor<string, []>("matrix_bd_11")];
            tensor<fp32, [1, 8, 188, 188]> var_652 = add(x = matrix_ac_5, y = matrix_bd_11)[name = tensor<string, []>("op_652")];
            tensor<fp32, []> _inversed_scores_9_y_0 = const()[name = tensor<string, []>("_inversed_scores_9_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_9 = mul(x = var_652, y = _inversed_scores_9_y_0)[name = tensor<string, []>("_inversed_scores_9")];
            tensor<fp32, [1, 8, 188, 188]> scores_11 = select(a = var_14, b = _inversed_scores_9, cond = mask_3)[name = tensor<string, []>("scores_11")];
            tensor<fp32, [1, 8, 188, 188]> var_658 = softmax(axis = var_32, x = scores_11)[name = tensor<string, []>("op_658")];
            tensor<fp32, [1, 8, 188, 188]> input_137 = select(a = var_13, b = var_658, cond = mask_3)[name = tensor<string, []>("input_137")];
            tensor<bool, []> x_59_transpose_x_0 = const()[name = tensor<string, []>("x_59_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_59_transpose_y_0 = const()[name = tensor<string, []>("x_59_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_7 = transpose(perm = value_7_perm_0, x = v_5)[name = tensor<string, []>("transpose_294")];
            tensor<fp32, [1, 8, 188, 128]> x_59 = matmul(transpose_x = x_59_transpose_x_0, transpose_y = x_59_transpose_y_0, x = input_137, y = value_7)[name = tensor<string, []>("x_59")];
            tensor<int32, [4]> var_662_perm_0 = const()[name = tensor<string, []>("op_662_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_663 = const()[name = tensor<string, []>("op_663"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_662 = transpose(perm = var_662_perm_0, x = x_59)[name = tensor<string, []>("transpose_293")];
            tensor<fp32, [1, 188, 1024]> input_139 = reshape(shape = var_663, x = var_662)[name = tensor<string, []>("input_139")];
            tensor<fp32, [1, 188, 1024]> input_141 = linear(bias = encoder_layers_2_self_attn_linear_out_bias, weight = encoder_layers_2_self_attn_linear_out_weight_quantized, x = input_139)[name = tensor<string, []>("linear_25")];
            tensor<fp32, [1, 188, 1024]> input_143 = add(x = input_135, y = input_141)[name = tensor<string, []>("input_143")];
            tensor<int32, [1]> x_63_axes_0 = const()[name = tensor<string, []>("x_63_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_63 = layer_norm(axes = x_63_axes_0, beta = encoder_layers_2_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_2_norm_conv_weight, x = input_143)[name = tensor<string, []>("x_63")];
            tensor<int32, [3]> input_145_perm_0 = const()[name = tensor<string, []>("input_145_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_147_pad_type_0 = const()[name = tensor<string, []>("input_147_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_147_strides_0 = const()[name = tensor<string, []>("input_147_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_147_pad_0 = const()[name = tensor<string, []>("input_147_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_147_dilations_0 = const()[name = tensor<string, []>("input_147_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_147_groups_0 = const()[name = tensor<string, []>("input_147_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_145 = transpose(perm = input_145_perm_0, x = x_63)[name = tensor<string, []>("transpose_292")];
            tensor<fp32, [1, 2048, 188]> input_147 = conv(bias = encoder_layers_2_conv_pointwise_conv1_bias, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = encoder_layers_2_conv_pointwise_conv1_weight_quantized, x = input_145)[name = tensor<string, []>("input_147")];
            tensor<int32, []> x_65_split_num_splits_0 = const()[name = tensor<string, []>("x_65_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_65_split_axis_0 = const()[name = tensor<string, []>("x_65_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_65_split_0, tensor<fp32, [1, 1024, 188]> x_65_split_1 = split(axis = x_65_split_axis_0, num_splits = x_65_split_num_splits_0, x = input_147)[name = tensor<string, []>("x_65_split")];
            tensor<fp32, [1, 1024, 188]> x_65_split_1_sigmoid = sigmoid(x = x_65_split_1)[name = tensor<string, []>("x_65_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_65 = mul(x = x_65_split_0, y = x_65_split_1_sigmoid)[name = tensor<string, []>("x_65")];
            tensor<fp32, [1, 1024, 188]> input_149 = select(a = var_13, b = x_65, cond = var_339)[name = tensor<string, []>("input_149")];
            tensor<fp32, []> const_37 = const()[name = tensor<string, []>("const_37"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_151_pad_0 = const()[name = tensor<string, []>("input_151_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_151_mode_0 = const()[name = tensor<string, []>("input_151_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_151 = pad(constant_val = const_37, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149)[name = tensor<string, []>("input_151")];
            tensor<string, []> input_153_pad_type_0 = const()[name = tensor<string, []>("input_153_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_153_groups_0 = const()[name = tensor<string, []>("input_153_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_153_strides_0 = const()[name = tensor<string, []>("input_153_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_153_pad_0 = const()[name = tensor<string, []>("input_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_153_dilations_0 = const()[name = tensor<string, []>("input_153_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_252_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_252_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588870336))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588880704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588879616)))];
            tensor<fp32, [1024]> const_253 = const()[name = tensor<string, []>("const_253"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588884864)))];
            tensor<fp32, [1, 1024, 188]> input_155 = conv(bias = const_253, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_quantized, x = input_151)[name = tensor<string, []>("input_155")];
            tensor<fp32, [1, 1024, 188]> input_157 = silu(x = input_155)[name = tensor<string, []>("input_157")];
            tensor<string, []> x_67_pad_type_0 = const()[name = tensor<string, []>("x_67_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_67_strides_0 = const()[name = tensor<string, []>("x_67_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_67_pad_0 = const()[name = tensor<string, []>("x_67_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_67_dilations_0 = const()[name = tensor<string, []>("x_67_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_67_groups_0 = const()[name = tensor<string, []>("x_67_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_67 = conv(bias = encoder_layers_2_conv_pointwise_conv2_bias, dilations = x_67_dilations_0, groups = x_67_groups_0, pad = x_67_pad_0, pad_type = x_67_pad_type_0, strides = x_67_strides_0, weight = encoder_layers_2_conv_pointwise_conv2_weight_quantized, x = input_157)[name = tensor<string, []>("x_67")];
            tensor<int32, [3]> input_159_perm_0 = const()[name = tensor<string, []>("input_159_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_159 = transpose(perm = input_159_perm_0, x = x_67)[name = tensor<string, []>("transpose_291")];
            tensor<fp32, [1, 188, 1024]> input_161 = add(x = input_143, y = input_159)[name = tensor<string, []>("input_161")];
            tensor<int32, [1]> input_163_axes_0 = const()[name = tensor<string, []>("input_163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_163 = layer_norm(axes = input_163_axes_0, beta = encoder_layers_2_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_2_norm_feed_forward2_weight, x = input_161)[name = tensor<string, []>("input_163")];
            tensor<fp32, [1, 188, 4096]> input_165 = linear(bias = encoder_layers_2_feed_forward2_linear1_bias, weight = encoder_layers_2_feed_forward2_linear1_weight_quantized, x = input_163)[name = tensor<string, []>("linear_26")];
            tensor<fp32, [1, 188, 4096]> input_167 = silu(x = input_165)[name = tensor<string, []>("input_167")];
            tensor<fp32, [1, 188, 1024]> input_171 = linear(bias = encoder_layers_2_feed_forward2_linear2_bias, weight = encoder_layers_2_feed_forward2_linear2_weight_quantized, x = input_167)[name = tensor<string, []>("linear_27")];
            tensor<fp32, []> var_729 = const()[name = tensor<string, []>("op_729"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_730 = mul(x = input_171, y = var_729)[name = tensor<string, []>("op_730")];
            tensor<fp32, [1, 188, 1024]> input_173 = add(x = input_161, y = var_730)[name = tensor<string, []>("input_173")];
            tensor<int32, [1]> input_175_axes_0 = const()[name = tensor<string, []>("input_175_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_175 = layer_norm(axes = input_175_axes_0, beta = encoder_layers_2_norm_out_bias, epsilon = var_11, gamma = encoder_layers_2_norm_out_weight, x = input_173)[name = tensor<string, []>("input_175")];
            tensor<int32, [1]> input_177_axes_0 = const()[name = tensor<string, []>("input_177_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_177 = layer_norm(axes = input_177_axes_0, beta = encoder_layers_3_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_3_norm_feed_forward1_weight, x = input_175)[name = tensor<string, []>("input_177")];
            tensor<fp32, [1, 188, 4096]> input_179 = linear(bias = encoder_layers_3_feed_forward1_linear1_bias, weight = encoder_layers_3_feed_forward1_linear1_weight_quantized, x = input_177)[name = tensor<string, []>("linear_28")];
            tensor<fp32, [1, 188, 4096]> input_181 = silu(x = input_179)[name = tensor<string, []>("input_181")];
            tensor<fp32, [1, 188, 1024]> input_185 = linear(bias = encoder_layers_3_feed_forward1_linear2_bias, weight = encoder_layers_3_feed_forward1_linear2_weight_quantized, x = input_181)[name = tensor<string, []>("linear_29")];
            tensor<fp32, []> var_760 = const()[name = tensor<string, []>("op_760"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_761 = mul(x = input_185, y = var_760)[name = tensor<string, []>("op_761")];
            tensor<fp32, [1, 188, 1024]> input_187 = add(x = input_175, y = var_761)[name = tensor<string, []>("input_187")];
            tensor<int32, [1]> query_7_axes_0 = const()[name = tensor<string, []>("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_7 = layer_norm(axes = query_7_axes_0, beta = encoder_layers_3_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_3_norm_self_att_weight, x = input_187)[name = tensor<string, []>("query_7")];
            tensor<fp32, [1, 188, 1024]> var_777 = linear(bias = encoder_layers_3_self_attn_linear_q_bias, weight = encoder_layers_3_self_attn_linear_q_weight_quantized, x = query_7)[name = tensor<string, []>("linear_30")];
            tensor<int32, [4]> var_778 = const()[name = tensor<string, []>("op_778"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_19 = reshape(shape = var_778, x = var_777)[name = tensor<string, []>("q_19")];
            tensor<fp32, [1, 188, 1024]> var_782 = linear(bias = encoder_layers_3_self_attn_linear_k_bias, weight = encoder_layers_3_self_attn_linear_k_weight_quantized, x = query_7)[name = tensor<string, []>("linear_31")];
            tensor<int32, [4]> var_783 = const()[name = tensor<string, []>("op_783"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_13 = reshape(shape = var_783, x = var_782)[name = tensor<string, []>("k_13")];
            tensor<fp32, [1, 188, 1024]> var_787 = linear(bias = encoder_layers_3_self_attn_linear_v_bias, weight = encoder_layers_3_self_attn_linear_v_weight_quantized, x = query_7)[name = tensor<string, []>("linear_32")];
            tensor<int32, [4]> var_788 = const()[name = tensor<string, []>("op_788"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_7 = reshape(shape = var_788, x = var_787)[name = tensor<string, []>("v_7")];
            tensor<int32, [4]> value_9_perm_0 = const()[name = tensor<string, []>("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_800 = add(x = q_19, y = encoder_layers_3_self_attn_pos_bias_u)[name = tensor<string, []>("op_800")];
            tensor<fp32, [1, 188, 8, 128]> var_802 = add(x = q_19, y = encoder_layers_3_self_attn_pos_bias_v)[name = tensor<string, []>("op_802")];
            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_804_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_804_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(588889024))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589273536))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589273088)))];
            tensor<bool, []> x_75_transpose_x_0 = const()[name = tensor<string, []>("x_75_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_75_transpose_y_0 = const()[name = tensor<string, []>("x_75_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_7 = transpose(perm = q_with_bias_v_7_perm_0, x = var_802)[name = tensor<string, []>("transpose_290")];
            tensor<fp32, [1, 8, 188, 375]> x_75 = matmul(transpose_x = x_75_transpose_x_0, transpose_y = x_75_transpose_y_0, x = q_with_bias_v_7, y = op_804_quantized)[name = tensor<string, []>("x_75")];
            tensor<fp32, []> const_44 = const()[name = tensor<string, []>("const_44"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_77_pad_0 = const()[name = tensor<string, []>("x_77_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_77_mode_0 = const()[name = tensor<string, []>("x_77_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_77 = pad(constant_val = const_44, mode = x_77_mode_0, pad = x_77_pad_0, x = x_75)[name = tensor<string, []>("x_77")];
            tensor<int32, [4]> var_812 = const()[name = tensor<string, []>("op_812"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_79 = reshape(shape = var_812, x = x_77)[name = tensor<string, []>("x_79")];
            tensor<int32, [4]> var_816_begin_0 = const()[name = tensor<string, []>("op_816_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_816_end_0 = const()[name = tensor<string, []>("op_816_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_816_end_mask_0 = const()[name = tensor<string, []>("op_816_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_816 = slice_by_index(begin = var_816_begin_0, end = var_816_end_0, end_mask = var_816_end_mask_0, x = x_79)[name = tensor<string, []>("op_816")];
            tensor<int32, [4]> var_817 = const()[name = tensor<string, []>("op_817"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_13 = reshape(shape = var_817, x = var_816)[name = tensor<string, []>("matrix_bd_13")];
            tensor<bool, []> matrix_ac_7_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_7_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_102_perm_0 = const()[name = tensor<string, []>("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_103_perm_0 = const()[name = tensor<string, []>("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13)[name = tensor<string, []>("transpose_288")];
            tensor<fp32, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_800)[name = tensor<string, []>("transpose_289")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_7 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = tensor<string, []>("matrix_ac_7")];
            tensor<int32, [4]> matrix_bd_15_begin_0 = const()[name = tensor<string, []>("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = tensor<string, []>("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_15 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13)[name = tensor<string, []>("matrix_bd_15")];
            tensor<fp32, [1, 8, 188, 188]> var_826 = add(x = matrix_ac_7, y = matrix_bd_15)[name = tensor<string, []>("op_826")];
            tensor<fp32, []> _inversed_scores_13_y_0 = const()[name = tensor<string, []>("_inversed_scores_13_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_13 = mul(x = var_826, y = _inversed_scores_13_y_0)[name = tensor<string, []>("_inversed_scores_13")];
            tensor<fp32, [1, 8, 188, 188]> scores_15 = select(a = var_14, b = _inversed_scores_13, cond = mask_3)[name = tensor<string, []>("scores_15")];
            tensor<fp32, [1, 8, 188, 188]> var_832 = softmax(axis = var_32, x = scores_15)[name = tensor<string, []>("op_832")];
            tensor<fp32, [1, 8, 188, 188]> input_189 = select(a = var_13, b = var_832, cond = mask_3)[name = tensor<string, []>("input_189")];
            tensor<bool, []> x_81_transpose_x_0 = const()[name = tensor<string, []>("x_81_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_81_transpose_y_0 = const()[name = tensor<string, []>("x_81_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_9 = transpose(perm = value_9_perm_0, x = v_7)[name = tensor<string, []>("transpose_287")];
            tensor<fp32, [1, 8, 188, 128]> x_81 = matmul(transpose_x = x_81_transpose_x_0, transpose_y = x_81_transpose_y_0, x = input_189, y = value_9)[name = tensor<string, []>("x_81")];
            tensor<int32, [4]> var_836_perm_0 = const()[name = tensor<string, []>("op_836_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_837 = const()[name = tensor<string, []>("op_837"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_836 = transpose(perm = var_836_perm_0, x = x_81)[name = tensor<string, []>("transpose_286")];
            tensor<fp32, [1, 188, 1024]> input_191 = reshape(shape = var_837, x = var_836)[name = tensor<string, []>("input_191")];
            tensor<fp32, [1, 188, 1024]> input_193 = linear(bias = encoder_layers_3_self_attn_linear_out_bias, weight = encoder_layers_3_self_attn_linear_out_weight_quantized, x = input_191)[name = tensor<string, []>("linear_34")];
            tensor<fp32, [1, 188, 1024]> input_195 = add(x = input_187, y = input_193)[name = tensor<string, []>("input_195")];
            tensor<int32, [1]> x_85_axes_0 = const()[name = tensor<string, []>("x_85_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_85 = layer_norm(axes = x_85_axes_0, beta = encoder_layers_3_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_3_norm_conv_weight, x = input_195)[name = tensor<string, []>("x_85")];
            tensor<int32, [3]> input_197_perm_0 = const()[name = tensor<string, []>("input_197_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_197 = transpose(perm = input_197_perm_0, x = x_85)[name = tensor<string, []>("transpose_285")];
            tensor<fp32, [1, 2048, 188]> input_199 = conv(bias = encoder_layers_3_conv_pointwise_conv1_bias, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = encoder_layers_3_conv_pointwise_conv1_weight_quantized, x = input_197)[name = tensor<string, []>("input_199")];
            tensor<int32, []> x_87_split_num_splits_0 = const()[name = tensor<string, []>("x_87_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_87_split_axis_0 = const()[name = tensor<string, []>("x_87_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_87_split_0, tensor<fp32, [1, 1024, 188]> x_87_split_1 = split(axis = x_87_split_axis_0, num_splits = x_87_split_num_splits_0, x = input_199)[name = tensor<string, []>("x_87_split")];
            tensor<fp32, [1, 1024, 188]> x_87_split_1_sigmoid = sigmoid(x = x_87_split_1)[name = tensor<string, []>("x_87_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_87 = mul(x = x_87_split_0, y = x_87_split_1_sigmoid)[name = tensor<string, []>("x_87")];
            tensor<fp32, [1, 1024, 188]> input_201 = select(a = var_13, b = x_87, cond = var_339)[name = tensor<string, []>("input_201")];
            tensor<fp32, []> const_47 = const()[name = tensor<string, []>("const_47"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_203_pad_0 = const()[name = tensor<string, []>("input_203_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_203_mode_0 = const()[name = tensor<string, []>("input_203_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_203 = pad(constant_val = const_47, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201)[name = tensor<string, []>("input_203")];
            tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_205_groups_0 = const()[name = tensor<string, []>("input_205_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_205_strides_0 = const()[name = tensor<string, []>("input_205_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_205_dilations_0 = const()[name = tensor<string, []>("input_205_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_254_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_254_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589275136))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589285504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589284416)))];
            tensor<fp32, [1024]> const_255 = const()[name = tensor<string, []>("const_255"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589289664)))];
            tensor<fp32, [1, 1024, 188]> input_207 = conv(bias = const_255, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_quantized, x = input_203)[name = tensor<string, []>("input_207")];
            tensor<fp32, [1, 1024, 188]> input_209 = silu(x = input_207)[name = tensor<string, []>("input_209")];
            tensor<string, []> x_89_pad_type_0 = const()[name = tensor<string, []>("x_89_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_89_strides_0 = const()[name = tensor<string, []>("x_89_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_89_pad_0 = const()[name = tensor<string, []>("x_89_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_89_dilations_0 = const()[name = tensor<string, []>("x_89_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_89_groups_0 = const()[name = tensor<string, []>("x_89_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_89 = conv(bias = encoder_layers_3_conv_pointwise_conv2_bias, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = encoder_layers_3_conv_pointwise_conv2_weight_quantized, x = input_209)[name = tensor<string, []>("x_89")];
            tensor<int32, [3]> input_211_perm_0 = const()[name = tensor<string, []>("input_211_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_211 = transpose(perm = input_211_perm_0, x = x_89)[name = tensor<string, []>("transpose_284")];
            tensor<fp32, [1, 188, 1024]> input_213 = add(x = input_195, y = input_211)[name = tensor<string, []>("input_213")];
            tensor<int32, [1]> input_215_axes_0 = const()[name = tensor<string, []>("input_215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_215 = layer_norm(axes = input_215_axes_0, beta = encoder_layers_3_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_3_norm_feed_forward2_weight, x = input_213)[name = tensor<string, []>("input_215")];
            tensor<fp32, [1, 188, 4096]> input_217 = linear(bias = encoder_layers_3_feed_forward2_linear1_bias, weight = encoder_layers_3_feed_forward2_linear1_weight_quantized, x = input_215)[name = tensor<string, []>("linear_35")];
            tensor<fp32, [1, 188, 4096]> input_219 = silu(x = input_217)[name = tensor<string, []>("input_219")];
            tensor<fp32, [1, 188, 1024]> input_223 = linear(bias = encoder_layers_3_feed_forward2_linear2_bias, weight = encoder_layers_3_feed_forward2_linear2_weight_quantized, x = input_219)[name = tensor<string, []>("linear_36")];
            tensor<fp32, []> var_903 = const()[name = tensor<string, []>("op_903"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_904 = mul(x = input_223, y = var_903)[name = tensor<string, []>("op_904")];
            tensor<fp32, [1, 188, 1024]> input_225 = add(x = input_213, y = var_904)[name = tensor<string, []>("input_225")];
            tensor<int32, [1]> input_227_axes_0 = const()[name = tensor<string, []>("input_227_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_227 = layer_norm(axes = input_227_axes_0, beta = encoder_layers_3_norm_out_bias, epsilon = var_11, gamma = encoder_layers_3_norm_out_weight, x = input_225)[name = tensor<string, []>("input_227")];
            tensor<int32, [1]> input_229_axes_0 = const()[name = tensor<string, []>("input_229_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_229 = layer_norm(axes = input_229_axes_0, beta = encoder_layers_4_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_4_norm_feed_forward1_weight, x = input_227)[name = tensor<string, []>("input_229")];
            tensor<fp32, [1, 188, 4096]> input_231 = linear(bias = encoder_layers_4_feed_forward1_linear1_bias, weight = encoder_layers_4_feed_forward1_linear1_weight_quantized, x = input_229)[name = tensor<string, []>("linear_37")];
            tensor<fp32, [1, 188, 4096]> input_233 = silu(x = input_231)[name = tensor<string, []>("input_233")];
            tensor<fp32, [1, 188, 1024]> input_237 = linear(bias = encoder_layers_4_feed_forward1_linear2_bias, weight = encoder_layers_4_feed_forward1_linear2_weight_quantized, x = input_233)[name = tensor<string, []>("linear_38")];
            tensor<fp32, []> var_934 = const()[name = tensor<string, []>("op_934"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_935 = mul(x = input_237, y = var_934)[name = tensor<string, []>("op_935")];
            tensor<fp32, [1, 188, 1024]> input_239 = add(x = input_227, y = var_935)[name = tensor<string, []>("input_239")];
            tensor<int32, [1]> query_9_axes_0 = const()[name = tensor<string, []>("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_9 = layer_norm(axes = query_9_axes_0, beta = encoder_layers_4_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_4_norm_self_att_weight, x = input_239)[name = tensor<string, []>("query_9")];
            tensor<fp32, [1, 188, 1024]> var_951 = linear(bias = encoder_layers_4_self_attn_linear_q_bias, weight = encoder_layers_4_self_attn_linear_q_weight_quantized, x = query_9)[name = tensor<string, []>("linear_39")];
            tensor<int32, [4]> var_952 = const()[name = tensor<string, []>("op_952"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_25 = reshape(shape = var_952, x = var_951)[name = tensor<string, []>("q_25")];
            tensor<fp32, [1, 188, 1024]> var_956 = linear(bias = encoder_layers_4_self_attn_linear_k_bias, weight = encoder_layers_4_self_attn_linear_k_weight_quantized, x = query_9)[name = tensor<string, []>("linear_40")];
            tensor<int32, [4]> var_957 = const()[name = tensor<string, []>("op_957"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_17 = reshape(shape = var_957, x = var_956)[name = tensor<string, []>("k_17")];
            tensor<fp32, [1, 188, 1024]> var_961 = linear(bias = encoder_layers_4_self_attn_linear_v_bias, weight = encoder_layers_4_self_attn_linear_v_weight_quantized, x = query_9)[name = tensor<string, []>("linear_41")];
            tensor<int32, [4]> var_962 = const()[name = tensor<string, []>("op_962"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_9 = reshape(shape = var_962, x = var_961)[name = tensor<string, []>("v_9")];
            tensor<int32, [4]> value_11_perm_0 = const()[name = tensor<string, []>("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_974 = add(x = q_25, y = encoder_layers_4_self_attn_pos_bias_u)[name = tensor<string, []>("op_974")];
            tensor<fp32, [1, 188, 8, 128]> var_976 = add(x = q_25, y = encoder_layers_4_self_attn_pos_bias_v)[name = tensor<string, []>("op_976")];
            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_978_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_978_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589293824))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589678336))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589677888)))];
            tensor<bool, []> x_97_transpose_x_0 = const()[name = tensor<string, []>("x_97_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_97_transpose_y_0 = const()[name = tensor<string, []>("x_97_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_9 = transpose(perm = q_with_bias_v_9_perm_0, x = var_976)[name = tensor<string, []>("transpose_283")];
            tensor<fp32, [1, 8, 188, 375]> x_97 = matmul(transpose_x = x_97_transpose_x_0, transpose_y = x_97_transpose_y_0, x = q_with_bias_v_9, y = op_978_quantized)[name = tensor<string, []>("x_97")];
            tensor<fp32, []> const_54 = const()[name = tensor<string, []>("const_54"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_99_pad_0 = const()[name = tensor<string, []>("x_99_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_99_mode_0 = const()[name = tensor<string, []>("x_99_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_99 = pad(constant_val = const_54, mode = x_99_mode_0, pad = x_99_pad_0, x = x_97)[name = tensor<string, []>("x_99")];
            tensor<int32, [4]> var_986 = const()[name = tensor<string, []>("op_986"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_101 = reshape(shape = var_986, x = x_99)[name = tensor<string, []>("x_101")];
            tensor<int32, [4]> var_990_begin_0 = const()[name = tensor<string, []>("op_990_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_990_end_0 = const()[name = tensor<string, []>("op_990_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_990_end_mask_0 = const()[name = tensor<string, []>("op_990_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_990 = slice_by_index(begin = var_990_begin_0, end = var_990_end_0, end_mask = var_990_end_mask_0, x = x_101)[name = tensor<string, []>("op_990")];
            tensor<int32, [4]> var_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_17 = reshape(shape = var_991, x = var_990)[name = tensor<string, []>("matrix_bd_17")];
            tensor<bool, []> matrix_ac_9_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_9_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_104_perm_0 = const()[name = tensor<string, []>("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_105_perm_0 = const()[name = tensor<string, []>("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17)[name = tensor<string, []>("transpose_281")];
            tensor<fp32, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_974)[name = tensor<string, []>("transpose_282")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_9 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = tensor<string, []>("matrix_ac_9")];
            tensor<int32, [4]> matrix_bd_19_begin_0 = const()[name = tensor<string, []>("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = tensor<string, []>("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_19 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17)[name = tensor<string, []>("matrix_bd_19")];
            tensor<fp32, [1, 8, 188, 188]> var_1000 = add(x = matrix_ac_9, y = matrix_bd_19)[name = tensor<string, []>("op_1000")];
            tensor<fp32, []> _inversed_scores_17_y_0 = const()[name = tensor<string, []>("_inversed_scores_17_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_17 = mul(x = var_1000, y = _inversed_scores_17_y_0)[name = tensor<string, []>("_inversed_scores_17")];
            tensor<fp32, [1, 8, 188, 188]> scores_19 = select(a = var_14, b = _inversed_scores_17, cond = mask_3)[name = tensor<string, []>("scores_19")];
            tensor<fp32, [1, 8, 188, 188]> var_1006 = softmax(axis = var_32, x = scores_19)[name = tensor<string, []>("op_1006")];
            tensor<fp32, [1, 8, 188, 188]> input_241 = select(a = var_13, b = var_1006, cond = mask_3)[name = tensor<string, []>("input_241")];
            tensor<bool, []> x_103_transpose_x_0 = const()[name = tensor<string, []>("x_103_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_103_transpose_y_0 = const()[name = tensor<string, []>("x_103_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_11 = transpose(perm = value_11_perm_0, x = v_9)[name = tensor<string, []>("transpose_280")];
            tensor<fp32, [1, 8, 188, 128]> x_103 = matmul(transpose_x = x_103_transpose_x_0, transpose_y = x_103_transpose_y_0, x = input_241, y = value_11)[name = tensor<string, []>("x_103")];
            tensor<int32, [4]> var_1010_perm_0 = const()[name = tensor<string, []>("op_1010_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1011 = const()[name = tensor<string, []>("op_1011"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_1010 = transpose(perm = var_1010_perm_0, x = x_103)[name = tensor<string, []>("transpose_279")];
            tensor<fp32, [1, 188, 1024]> input_243 = reshape(shape = var_1011, x = var_1010)[name = tensor<string, []>("input_243")];
            tensor<fp32, [1, 188, 1024]> input_245 = linear(bias = encoder_layers_4_self_attn_linear_out_bias, weight = encoder_layers_4_self_attn_linear_out_weight_quantized, x = input_243)[name = tensor<string, []>("linear_43")];
            tensor<fp32, [1, 188, 1024]> input_247 = add(x = input_239, y = input_245)[name = tensor<string, []>("input_247")];
            tensor<int32, [1]> x_107_axes_0 = const()[name = tensor<string, []>("x_107_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_107 = layer_norm(axes = x_107_axes_0, beta = encoder_layers_4_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_4_norm_conv_weight, x = input_247)[name = tensor<string, []>("x_107")];
            tensor<int32, [3]> input_249_perm_0 = const()[name = tensor<string, []>("input_249_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_251_pad_type_0 = const()[name = tensor<string, []>("input_251_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_251_strides_0 = const()[name = tensor<string, []>("input_251_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_251_pad_0 = const()[name = tensor<string, []>("input_251_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_251_dilations_0 = const()[name = tensor<string, []>("input_251_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_251_groups_0 = const()[name = tensor<string, []>("input_251_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_249 = transpose(perm = input_249_perm_0, x = x_107)[name = tensor<string, []>("transpose_278")];
            tensor<fp32, [1, 2048, 188]> input_251 = conv(bias = encoder_layers_4_conv_pointwise_conv1_bias, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = encoder_layers_4_conv_pointwise_conv1_weight_quantized, x = input_249)[name = tensor<string, []>("input_251")];
            tensor<int32, []> x_109_split_num_splits_0 = const()[name = tensor<string, []>("x_109_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_109_split_axis_0 = const()[name = tensor<string, []>("x_109_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_109_split_0, tensor<fp32, [1, 1024, 188]> x_109_split_1 = split(axis = x_109_split_axis_0, num_splits = x_109_split_num_splits_0, x = input_251)[name = tensor<string, []>("x_109_split")];
            tensor<fp32, [1, 1024, 188]> x_109_split_1_sigmoid = sigmoid(x = x_109_split_1)[name = tensor<string, []>("x_109_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_109 = mul(x = x_109_split_0, y = x_109_split_1_sigmoid)[name = tensor<string, []>("x_109")];
            tensor<fp32, [1, 1024, 188]> input_253 = select(a = var_13, b = x_109, cond = var_339)[name = tensor<string, []>("input_253")];
            tensor<fp32, []> const_57 = const()[name = tensor<string, []>("const_57"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_255_pad_0 = const()[name = tensor<string, []>("input_255_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_255_mode_0 = const()[name = tensor<string, []>("input_255_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_255 = pad(constant_val = const_57, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253)[name = tensor<string, []>("input_255")];
            tensor<string, []> input_257_pad_type_0 = const()[name = tensor<string, []>("input_257_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_257_groups_0 = const()[name = tensor<string, []>("input_257_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_257_strides_0 = const()[name = tensor<string, []>("input_257_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_257_pad_0 = const()[name = tensor<string, []>("input_257_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_257_dilations_0 = const()[name = tensor<string, []>("input_257_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_256_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_256_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589679936))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589690304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589689216)))];
            tensor<fp32, [1024]> const_257 = const()[name = tensor<string, []>("const_257"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589694464)))];
            tensor<fp32, [1, 1024, 188]> input_259 = conv(bias = const_257, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_quantized, x = input_255)[name = tensor<string, []>("input_259")];
            tensor<fp32, [1, 1024, 188]> input_261 = silu(x = input_259)[name = tensor<string, []>("input_261")];
            tensor<string, []> x_111_pad_type_0 = const()[name = tensor<string, []>("x_111_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_111_strides_0 = const()[name = tensor<string, []>("x_111_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_111_pad_0 = const()[name = tensor<string, []>("x_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_111_dilations_0 = const()[name = tensor<string, []>("x_111_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_111_groups_0 = const()[name = tensor<string, []>("x_111_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_111 = conv(bias = encoder_layers_4_conv_pointwise_conv2_bias, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = encoder_layers_4_conv_pointwise_conv2_weight_quantized, x = input_261)[name = tensor<string, []>("x_111")];
            tensor<int32, [3]> input_263_perm_0 = const()[name = tensor<string, []>("input_263_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_263 = transpose(perm = input_263_perm_0, x = x_111)[name = tensor<string, []>("transpose_277")];
            tensor<fp32, [1, 188, 1024]> input_265 = add(x = input_247, y = input_263)[name = tensor<string, []>("input_265")];
            tensor<int32, [1]> input_267_axes_0 = const()[name = tensor<string, []>("input_267_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_267 = layer_norm(axes = input_267_axes_0, beta = encoder_layers_4_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_4_norm_feed_forward2_weight, x = input_265)[name = tensor<string, []>("input_267")];
            tensor<fp32, [1, 188, 4096]> input_269 = linear(bias = encoder_layers_4_feed_forward2_linear1_bias, weight = encoder_layers_4_feed_forward2_linear1_weight_quantized, x = input_267)[name = tensor<string, []>("linear_44")];
            tensor<fp32, [1, 188, 4096]> input_271 = silu(x = input_269)[name = tensor<string, []>("input_271")];
            tensor<fp32, [1, 188, 1024]> input_275 = linear(bias = encoder_layers_4_feed_forward2_linear2_bias, weight = encoder_layers_4_feed_forward2_linear2_weight_quantized, x = input_271)[name = tensor<string, []>("linear_45")];
            tensor<fp32, []> var_1077 = const()[name = tensor<string, []>("op_1077"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1078 = mul(x = input_275, y = var_1077)[name = tensor<string, []>("op_1078")];
            tensor<fp32, [1, 188, 1024]> input_277 = add(x = input_265, y = var_1078)[name = tensor<string, []>("input_277")];
            tensor<int32, [1]> input_279_axes_0 = const()[name = tensor<string, []>("input_279_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_279 = layer_norm(axes = input_279_axes_0, beta = encoder_layers_4_norm_out_bias, epsilon = var_11, gamma = encoder_layers_4_norm_out_weight, x = input_277)[name = tensor<string, []>("input_279")];
            tensor<int32, [1]> input_281_axes_0 = const()[name = tensor<string, []>("input_281_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_281 = layer_norm(axes = input_281_axes_0, beta = encoder_layers_5_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_5_norm_feed_forward1_weight, x = input_279)[name = tensor<string, []>("input_281")];
            tensor<fp32, [1, 188, 4096]> input_283 = linear(bias = encoder_layers_5_feed_forward1_linear1_bias, weight = encoder_layers_5_feed_forward1_linear1_weight_quantized, x = input_281)[name = tensor<string, []>("linear_46")];
            tensor<fp32, [1, 188, 4096]> input_285 = silu(x = input_283)[name = tensor<string, []>("input_285")];
            tensor<fp32, [1, 188, 1024]> input_289 = linear(bias = encoder_layers_5_feed_forward1_linear2_bias, weight = encoder_layers_5_feed_forward1_linear2_weight_quantized, x = input_285)[name = tensor<string, []>("linear_47")];
            tensor<fp32, []> var_1108 = const()[name = tensor<string, []>("op_1108"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1109 = mul(x = input_289, y = var_1108)[name = tensor<string, []>("op_1109")];
            tensor<fp32, [1, 188, 1024]> input_291 = add(x = input_279, y = var_1109)[name = tensor<string, []>("input_291")];
            tensor<int32, [1]> query_11_axes_0 = const()[name = tensor<string, []>("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_11 = layer_norm(axes = query_11_axes_0, beta = encoder_layers_5_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_5_norm_self_att_weight, x = input_291)[name = tensor<string, []>("query_11")];
            tensor<fp32, [1, 188, 1024]> var_1125 = linear(bias = encoder_layers_5_self_attn_linear_q_bias, weight = encoder_layers_5_self_attn_linear_q_weight_quantized, x = query_11)[name = tensor<string, []>("linear_48")];
            tensor<int32, [4]> var_1126 = const()[name = tensor<string, []>("op_1126"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_31 = reshape(shape = var_1126, x = var_1125)[name = tensor<string, []>("q_31")];
            tensor<fp32, [1, 188, 1024]> var_1130 = linear(bias = encoder_layers_5_self_attn_linear_k_bias, weight = encoder_layers_5_self_attn_linear_k_weight_quantized, x = query_11)[name = tensor<string, []>("linear_49")];
            tensor<int32, [4]> var_1131 = const()[name = tensor<string, []>("op_1131"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_21 = reshape(shape = var_1131, x = var_1130)[name = tensor<string, []>("k_21")];
            tensor<fp32, [1, 188, 1024]> var_1135 = linear(bias = encoder_layers_5_self_attn_linear_v_bias, weight = encoder_layers_5_self_attn_linear_v_weight_quantized, x = query_11)[name = tensor<string, []>("linear_50")];
            tensor<int32, [4]> var_1136 = const()[name = tensor<string, []>("op_1136"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_11 = reshape(shape = var_1136, x = var_1135)[name = tensor<string, []>("v_11")];
            tensor<int32, [4]> value_13_perm_0 = const()[name = tensor<string, []>("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_1148 = add(x = q_31, y = encoder_layers_5_self_attn_pos_bias_u)[name = tensor<string, []>("op_1148")];
            tensor<fp32, [1, 188, 8, 128]> var_1150 = add(x = q_31, y = encoder_layers_5_self_attn_pos_bias_v)[name = tensor<string, []>("op_1150")];
            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_1152_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1152_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589698624))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590083136))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590082688)))];
            tensor<bool, []> x_119_transpose_x_0 = const()[name = tensor<string, []>("x_119_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_119_transpose_y_0 = const()[name = tensor<string, []>("x_119_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_11 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1150)[name = tensor<string, []>("transpose_276")];
            tensor<fp32, [1, 8, 188, 375]> x_119 = matmul(transpose_x = x_119_transpose_x_0, transpose_y = x_119_transpose_y_0, x = q_with_bias_v_11, y = op_1152_quantized)[name = tensor<string, []>("x_119")];
            tensor<fp32, []> const_64 = const()[name = tensor<string, []>("const_64"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_121_pad_0 = const()[name = tensor<string, []>("x_121_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_121_mode_0 = const()[name = tensor<string, []>("x_121_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_121 = pad(constant_val = const_64, mode = x_121_mode_0, pad = x_121_pad_0, x = x_119)[name = tensor<string, []>("x_121")];
            tensor<int32, [4]> var_1160 = const()[name = tensor<string, []>("op_1160"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_123 = reshape(shape = var_1160, x = x_121)[name = tensor<string, []>("x_123")];
            tensor<int32, [4]> var_1164_begin_0 = const()[name = tensor<string, []>("op_1164_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1164_end_0 = const()[name = tensor<string, []>("op_1164_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1164_end_mask_0 = const()[name = tensor<string, []>("op_1164_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_1164 = slice_by_index(begin = var_1164_begin_0, end = var_1164_end_0, end_mask = var_1164_end_mask_0, x = x_123)[name = tensor<string, []>("op_1164")];
            tensor<int32, [4]> var_1165 = const()[name = tensor<string, []>("op_1165"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_21 = reshape(shape = var_1165, x = var_1164)[name = tensor<string, []>("matrix_bd_21")];
            tensor<bool, []> matrix_ac_11_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_11_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_11_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_11_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_106_perm_0 = const()[name = tensor<string, []>("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_107_perm_0 = const()[name = tensor<string, []>("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21)[name = tensor<string, []>("transpose_274")];
            tensor<fp32, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1148)[name = tensor<string, []>("transpose_275")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_11 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = tensor<string, []>("matrix_ac_11")];
            tensor<int32, [4]> matrix_bd_23_begin_0 = const()[name = tensor<string, []>("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = tensor<string, []>("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_23 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21)[name = tensor<string, []>("matrix_bd_23")];
            tensor<fp32, [1, 8, 188, 188]> var_1174 = add(x = matrix_ac_11, y = matrix_bd_23)[name = tensor<string, []>("op_1174")];
            tensor<fp32, []> _inversed_scores_21_y_0 = const()[name = tensor<string, []>("_inversed_scores_21_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_21 = mul(x = var_1174, y = _inversed_scores_21_y_0)[name = tensor<string, []>("_inversed_scores_21")];
            tensor<fp32, [1, 8, 188, 188]> scores_23 = select(a = var_14, b = _inversed_scores_21, cond = mask_3)[name = tensor<string, []>("scores_23")];
            tensor<fp32, [1, 8, 188, 188]> var_1180 = softmax(axis = var_32, x = scores_23)[name = tensor<string, []>("op_1180")];
            tensor<fp32, [1, 8, 188, 188]> input_293 = select(a = var_13, b = var_1180, cond = mask_3)[name = tensor<string, []>("input_293")];
            tensor<bool, []> x_125_transpose_x_0 = const()[name = tensor<string, []>("x_125_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_125_transpose_y_0 = const()[name = tensor<string, []>("x_125_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_13 = transpose(perm = value_13_perm_0, x = v_11)[name = tensor<string, []>("transpose_273")];
            tensor<fp32, [1, 8, 188, 128]> x_125 = matmul(transpose_x = x_125_transpose_x_0, transpose_y = x_125_transpose_y_0, x = input_293, y = value_13)[name = tensor<string, []>("x_125")];
            tensor<int32, [4]> var_1184_perm_0 = const()[name = tensor<string, []>("op_1184_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1185 = const()[name = tensor<string, []>("op_1185"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_1184 = transpose(perm = var_1184_perm_0, x = x_125)[name = tensor<string, []>("transpose_272")];
            tensor<fp32, [1, 188, 1024]> input_295 = reshape(shape = var_1185, x = var_1184)[name = tensor<string, []>("input_295")];
            tensor<fp32, [1, 188, 1024]> input_297 = linear(bias = encoder_layers_5_self_attn_linear_out_bias, weight = encoder_layers_5_self_attn_linear_out_weight_quantized, x = input_295)[name = tensor<string, []>("linear_52")];
            tensor<fp32, [1, 188, 1024]> input_299 = add(x = input_291, y = input_297)[name = tensor<string, []>("input_299")];
            tensor<int32, [1]> x_129_axes_0 = const()[name = tensor<string, []>("x_129_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_129 = layer_norm(axes = x_129_axes_0, beta = encoder_layers_5_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_5_norm_conv_weight, x = input_299)[name = tensor<string, []>("x_129")];
            tensor<int32, [3]> input_301_perm_0 = const()[name = tensor<string, []>("input_301_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_303_pad_type_0 = const()[name = tensor<string, []>("input_303_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_303_strides_0 = const()[name = tensor<string, []>("input_303_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_303_pad_0 = const()[name = tensor<string, []>("input_303_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_303_dilations_0 = const()[name = tensor<string, []>("input_303_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_303_groups_0 = const()[name = tensor<string, []>("input_303_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_301 = transpose(perm = input_301_perm_0, x = x_129)[name = tensor<string, []>("transpose_271")];
            tensor<fp32, [1, 2048, 188]> input_303 = conv(bias = encoder_layers_5_conv_pointwise_conv1_bias, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = encoder_layers_5_conv_pointwise_conv1_weight_quantized, x = input_301)[name = tensor<string, []>("input_303")];
            tensor<int32, []> x_131_split_num_splits_0 = const()[name = tensor<string, []>("x_131_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_131_split_axis_0 = const()[name = tensor<string, []>("x_131_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_131_split_0, tensor<fp32, [1, 1024, 188]> x_131_split_1 = split(axis = x_131_split_axis_0, num_splits = x_131_split_num_splits_0, x = input_303)[name = tensor<string, []>("x_131_split")];
            tensor<fp32, [1, 1024, 188]> x_131_split_1_sigmoid = sigmoid(x = x_131_split_1)[name = tensor<string, []>("x_131_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_131 = mul(x = x_131_split_0, y = x_131_split_1_sigmoid)[name = tensor<string, []>("x_131")];
            tensor<fp32, [1, 1024, 188]> input_305 = select(a = var_13, b = x_131, cond = var_339)[name = tensor<string, []>("input_305")];
            tensor<fp32, []> const_67 = const()[name = tensor<string, []>("const_67"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_307_pad_0 = const()[name = tensor<string, []>("input_307_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_307_mode_0 = const()[name = tensor<string, []>("input_307_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_307 = pad(constant_val = const_67, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305)[name = tensor<string, []>("input_307")];
            tensor<string, []> input_309_pad_type_0 = const()[name = tensor<string, []>("input_309_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_309_groups_0 = const()[name = tensor<string, []>("input_309_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_309_strides_0 = const()[name = tensor<string, []>("input_309_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_309_pad_0 = const()[name = tensor<string, []>("input_309_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_309_dilations_0 = const()[name = tensor<string, []>("input_309_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_258_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_258_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590084736))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590095104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590094016)))];
            tensor<fp32, [1024]> const_259 = const()[name = tensor<string, []>("const_259"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590099264)))];
            tensor<fp32, [1, 1024, 188]> input_311 = conv(bias = const_259, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_quantized, x = input_307)[name = tensor<string, []>("input_311")];
            tensor<fp32, [1, 1024, 188]> input_313 = silu(x = input_311)[name = tensor<string, []>("input_313")];
            tensor<string, []> x_133_pad_type_0 = const()[name = tensor<string, []>("x_133_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_133_strides_0 = const()[name = tensor<string, []>("x_133_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_133_pad_0 = const()[name = tensor<string, []>("x_133_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_133_dilations_0 = const()[name = tensor<string, []>("x_133_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_133_groups_0 = const()[name = tensor<string, []>("x_133_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_133 = conv(bias = encoder_layers_5_conv_pointwise_conv2_bias, dilations = x_133_dilations_0, groups = x_133_groups_0, pad = x_133_pad_0, pad_type = x_133_pad_type_0, strides = x_133_strides_0, weight = encoder_layers_5_conv_pointwise_conv2_weight_quantized, x = input_313)[name = tensor<string, []>("x_133")];
            tensor<int32, [3]> input_315_perm_0 = const()[name = tensor<string, []>("input_315_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_315 = transpose(perm = input_315_perm_0, x = x_133)[name = tensor<string, []>("transpose_270")];
            tensor<fp32, [1, 188, 1024]> input_317 = add(x = input_299, y = input_315)[name = tensor<string, []>("input_317")];
            tensor<int32, [1]> input_319_axes_0 = const()[name = tensor<string, []>("input_319_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_319 = layer_norm(axes = input_319_axes_0, beta = encoder_layers_5_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_5_norm_feed_forward2_weight, x = input_317)[name = tensor<string, []>("input_319")];
            tensor<fp32, [1, 188, 4096]> input_321 = linear(bias = encoder_layers_5_feed_forward2_linear1_bias, weight = encoder_layers_5_feed_forward2_linear1_weight_quantized, x = input_319)[name = tensor<string, []>("linear_53")];
            tensor<fp32, [1, 188, 4096]> input_323 = silu(x = input_321)[name = tensor<string, []>("input_323")];
            tensor<fp32, [1, 188, 1024]> input_327 = linear(bias = encoder_layers_5_feed_forward2_linear2_bias, weight = encoder_layers_5_feed_forward2_linear2_weight_quantized, x = input_323)[name = tensor<string, []>("linear_54")];
            tensor<fp32, []> var_1251 = const()[name = tensor<string, []>("op_1251"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1252 = mul(x = input_327, y = var_1251)[name = tensor<string, []>("op_1252")];
            tensor<fp32, [1, 188, 1024]> input_329 = add(x = input_317, y = var_1252)[name = tensor<string, []>("input_329")];
            tensor<int32, [1]> input_331_axes_0 = const()[name = tensor<string, []>("input_331_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_331 = layer_norm(axes = input_331_axes_0, beta = encoder_layers_5_norm_out_bias, epsilon = var_11, gamma = encoder_layers_5_norm_out_weight, x = input_329)[name = tensor<string, []>("input_331")];
            tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_333 = layer_norm(axes = input_333_axes_0, beta = encoder_layers_6_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_6_norm_feed_forward1_weight, x = input_331)[name = tensor<string, []>("input_333")];
            tensor<fp32, [1, 188, 4096]> input_335 = linear(bias = encoder_layers_6_feed_forward1_linear1_bias, weight = encoder_layers_6_feed_forward1_linear1_weight_quantized, x = input_333)[name = tensor<string, []>("linear_55")];
            tensor<fp32, [1, 188, 4096]> input_337 = silu(x = input_335)[name = tensor<string, []>("input_337")];
            tensor<fp32, [1, 188, 1024]> input_341 = linear(bias = encoder_layers_6_feed_forward1_linear2_bias, weight = encoder_layers_6_feed_forward1_linear2_weight_quantized, x = input_337)[name = tensor<string, []>("linear_56")];
            tensor<fp32, []> var_1282 = const()[name = tensor<string, []>("op_1282"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1283 = mul(x = input_341, y = var_1282)[name = tensor<string, []>("op_1283")];
            tensor<fp32, [1, 188, 1024]> input_343 = add(x = input_331, y = var_1283)[name = tensor<string, []>("input_343")];
            tensor<int32, [1]> query_13_axes_0 = const()[name = tensor<string, []>("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_13 = layer_norm(axes = query_13_axes_0, beta = encoder_layers_6_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_6_norm_self_att_weight, x = input_343)[name = tensor<string, []>("query_13")];
            tensor<fp32, [1, 188, 1024]> var_1299 = linear(bias = encoder_layers_6_self_attn_linear_q_bias, weight = encoder_layers_6_self_attn_linear_q_weight_quantized, x = query_13)[name = tensor<string, []>("linear_57")];
            tensor<int32, [4]> var_1300 = const()[name = tensor<string, []>("op_1300"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_37 = reshape(shape = var_1300, x = var_1299)[name = tensor<string, []>("q_37")];
            tensor<fp32, [1, 188, 1024]> var_1304 = linear(bias = encoder_layers_6_self_attn_linear_k_bias, weight = encoder_layers_6_self_attn_linear_k_weight_quantized, x = query_13)[name = tensor<string, []>("linear_58")];
            tensor<int32, [4]> var_1305 = const()[name = tensor<string, []>("op_1305"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_25 = reshape(shape = var_1305, x = var_1304)[name = tensor<string, []>("k_25")];
            tensor<fp32, [1, 188, 1024]> var_1309 = linear(bias = encoder_layers_6_self_attn_linear_v_bias, weight = encoder_layers_6_self_attn_linear_v_weight_quantized, x = query_13)[name = tensor<string, []>("linear_59")];
            tensor<int32, [4]> var_1310 = const()[name = tensor<string, []>("op_1310"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_13 = reshape(shape = var_1310, x = var_1309)[name = tensor<string, []>("v_13")];
            tensor<int32, [4]> value_15_perm_0 = const()[name = tensor<string, []>("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_1322 = add(x = q_37, y = encoder_layers_6_self_attn_pos_bias_u)[name = tensor<string, []>("op_1322")];
            tensor<fp32, [1, 188, 8, 128]> var_1324 = add(x = q_37, y = encoder_layers_6_self_attn_pos_bias_v)[name = tensor<string, []>("op_1324")];
            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_1326_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1326_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590103424))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590487936))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590487488)))];
            tensor<bool, []> x_141_transpose_x_0 = const()[name = tensor<string, []>("x_141_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_141_transpose_y_0 = const()[name = tensor<string, []>("x_141_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_13 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1324)[name = tensor<string, []>("transpose_269")];
            tensor<fp32, [1, 8, 188, 375]> x_141 = matmul(transpose_x = x_141_transpose_x_0, transpose_y = x_141_transpose_y_0, x = q_with_bias_v_13, y = op_1326_quantized)[name = tensor<string, []>("x_141")];
            tensor<fp32, []> const_74 = const()[name = tensor<string, []>("const_74"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_143_pad_0 = const()[name = tensor<string, []>("x_143_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_143_mode_0 = const()[name = tensor<string, []>("x_143_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_143 = pad(constant_val = const_74, mode = x_143_mode_0, pad = x_143_pad_0, x = x_141)[name = tensor<string, []>("x_143")];
            tensor<int32, [4]> var_1334 = const()[name = tensor<string, []>("op_1334"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_145 = reshape(shape = var_1334, x = x_143)[name = tensor<string, []>("x_145")];
            tensor<int32, [4]> var_1338_begin_0 = const()[name = tensor<string, []>("op_1338_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1338_end_0 = const()[name = tensor<string, []>("op_1338_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1338_end_mask_0 = const()[name = tensor<string, []>("op_1338_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_1338 = slice_by_index(begin = var_1338_begin_0, end = var_1338_end_0, end_mask = var_1338_end_mask_0, x = x_145)[name = tensor<string, []>("op_1338")];
            tensor<int32, [4]> var_1339 = const()[name = tensor<string, []>("op_1339"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_25 = reshape(shape = var_1339, x = var_1338)[name = tensor<string, []>("matrix_bd_25")];
            tensor<bool, []> matrix_ac_13_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_13_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_108_perm_0 = const()[name = tensor<string, []>("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_109_perm_0 = const()[name = tensor<string, []>("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25)[name = tensor<string, []>("transpose_267")];
            tensor<fp32, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1322)[name = tensor<string, []>("transpose_268")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_13 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = tensor<string, []>("matrix_ac_13")];
            tensor<int32, [4]> matrix_bd_27_begin_0 = const()[name = tensor<string, []>("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = tensor<string, []>("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_27 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25)[name = tensor<string, []>("matrix_bd_27")];
            tensor<fp32, [1, 8, 188, 188]> var_1348 = add(x = matrix_ac_13, y = matrix_bd_27)[name = tensor<string, []>("op_1348")];
            tensor<fp32, []> _inversed_scores_25_y_0 = const()[name = tensor<string, []>("_inversed_scores_25_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_25 = mul(x = var_1348, y = _inversed_scores_25_y_0)[name = tensor<string, []>("_inversed_scores_25")];
            tensor<fp32, [1, 8, 188, 188]> scores_27 = select(a = var_14, b = _inversed_scores_25, cond = mask_3)[name = tensor<string, []>("scores_27")];
            tensor<fp32, [1, 8, 188, 188]> var_1354 = softmax(axis = var_32, x = scores_27)[name = tensor<string, []>("op_1354")];
            tensor<fp32, [1, 8, 188, 188]> input_345 = select(a = var_13, b = var_1354, cond = mask_3)[name = tensor<string, []>("input_345")];
            tensor<bool, []> x_147_transpose_x_0 = const()[name = tensor<string, []>("x_147_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_147_transpose_y_0 = const()[name = tensor<string, []>("x_147_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_15 = transpose(perm = value_15_perm_0, x = v_13)[name = tensor<string, []>("transpose_266")];
            tensor<fp32, [1, 8, 188, 128]> x_147 = matmul(transpose_x = x_147_transpose_x_0, transpose_y = x_147_transpose_y_0, x = input_345, y = value_15)[name = tensor<string, []>("x_147")];
            tensor<int32, [4]> var_1358_perm_0 = const()[name = tensor<string, []>("op_1358_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1359 = const()[name = tensor<string, []>("op_1359"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_1358 = transpose(perm = var_1358_perm_0, x = x_147)[name = tensor<string, []>("transpose_265")];
            tensor<fp32, [1, 188, 1024]> input_347 = reshape(shape = var_1359, x = var_1358)[name = tensor<string, []>("input_347")];
            tensor<fp32, [1, 188, 1024]> input_349 = linear(bias = encoder_layers_6_self_attn_linear_out_bias, weight = encoder_layers_6_self_attn_linear_out_weight_quantized, x = input_347)[name = tensor<string, []>("linear_61")];
            tensor<fp32, [1, 188, 1024]> input_351 = add(x = input_343, y = input_349)[name = tensor<string, []>("input_351")];
            tensor<int32, [1]> x_151_axes_0 = const()[name = tensor<string, []>("x_151_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_151 = layer_norm(axes = x_151_axes_0, beta = encoder_layers_6_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_6_norm_conv_weight, x = input_351)[name = tensor<string, []>("x_151")];
            tensor<int32, [3]> input_353_perm_0 = const()[name = tensor<string, []>("input_353_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_355_pad_type_0 = const()[name = tensor<string, []>("input_355_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_355_strides_0 = const()[name = tensor<string, []>("input_355_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_355_pad_0 = const()[name = tensor<string, []>("input_355_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_355_dilations_0 = const()[name = tensor<string, []>("input_355_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_355_groups_0 = const()[name = tensor<string, []>("input_355_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_353 = transpose(perm = input_353_perm_0, x = x_151)[name = tensor<string, []>("transpose_264")];
            tensor<fp32, [1, 2048, 188]> input_355 = conv(bias = encoder_layers_6_conv_pointwise_conv1_bias, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = encoder_layers_6_conv_pointwise_conv1_weight_quantized, x = input_353)[name = tensor<string, []>("input_355")];
            tensor<int32, []> x_153_split_num_splits_0 = const()[name = tensor<string, []>("x_153_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_153_split_axis_0 = const()[name = tensor<string, []>("x_153_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_153_split_0, tensor<fp32, [1, 1024, 188]> x_153_split_1 = split(axis = x_153_split_axis_0, num_splits = x_153_split_num_splits_0, x = input_355)[name = tensor<string, []>("x_153_split")];
            tensor<fp32, [1, 1024, 188]> x_153_split_1_sigmoid = sigmoid(x = x_153_split_1)[name = tensor<string, []>("x_153_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_153 = mul(x = x_153_split_0, y = x_153_split_1_sigmoid)[name = tensor<string, []>("x_153")];
            tensor<fp32, [1, 1024, 188]> input_357 = select(a = var_13, b = x_153, cond = var_339)[name = tensor<string, []>("input_357")];
            tensor<fp32, []> const_77 = const()[name = tensor<string, []>("const_77"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_359_pad_0 = const()[name = tensor<string, []>("input_359_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_359_mode_0 = const()[name = tensor<string, []>("input_359_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_359 = pad(constant_val = const_77, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357)[name = tensor<string, []>("input_359")];
            tensor<string, []> input_361_pad_type_0 = const()[name = tensor<string, []>("input_361_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_361_groups_0 = const()[name = tensor<string, []>("input_361_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_361_strides_0 = const()[name = tensor<string, []>("input_361_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_361_pad_0 = const()[name = tensor<string, []>("input_361_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_361_dilations_0 = const()[name = tensor<string, []>("input_361_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_260_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_260_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590489536))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590499904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590498816)))];
            tensor<fp32, [1024]> const_261 = const()[name = tensor<string, []>("const_261"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590504064)))];
            tensor<fp32, [1, 1024, 188]> input_363 = conv(bias = const_261, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_quantized, x = input_359)[name = tensor<string, []>("input_363")];
            tensor<fp32, [1, 1024, 188]> input_365 = silu(x = input_363)[name = tensor<string, []>("input_365")];
            tensor<string, []> x_155_pad_type_0 = const()[name = tensor<string, []>("x_155_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_155_strides_0 = const()[name = tensor<string, []>("x_155_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_155_pad_0 = const()[name = tensor<string, []>("x_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_155_dilations_0 = const()[name = tensor<string, []>("x_155_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_155_groups_0 = const()[name = tensor<string, []>("x_155_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_155 = conv(bias = encoder_layers_6_conv_pointwise_conv2_bias, dilations = x_155_dilations_0, groups = x_155_groups_0, pad = x_155_pad_0, pad_type = x_155_pad_type_0, strides = x_155_strides_0, weight = encoder_layers_6_conv_pointwise_conv2_weight_quantized, x = input_365)[name = tensor<string, []>("x_155")];
            tensor<int32, [3]> input_367_perm_0 = const()[name = tensor<string, []>("input_367_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_367 = transpose(perm = input_367_perm_0, x = x_155)[name = tensor<string, []>("transpose_263")];
            tensor<fp32, [1, 188, 1024]> input_369 = add(x = input_351, y = input_367)[name = tensor<string, []>("input_369")];
            tensor<int32, [1]> input_371_axes_0 = const()[name = tensor<string, []>("input_371_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_371 = layer_norm(axes = input_371_axes_0, beta = encoder_layers_6_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_6_norm_feed_forward2_weight, x = input_369)[name = tensor<string, []>("input_371")];
            tensor<fp32, [1, 188, 4096]> input_373 = linear(bias = encoder_layers_6_feed_forward2_linear1_bias, weight = encoder_layers_6_feed_forward2_linear1_weight_quantized, x = input_371)[name = tensor<string, []>("linear_62")];
            tensor<fp32, [1, 188, 4096]> input_375 = silu(x = input_373)[name = tensor<string, []>("input_375")];
            tensor<fp32, [1, 188, 1024]> input_379 = linear(bias = encoder_layers_6_feed_forward2_linear2_bias, weight = encoder_layers_6_feed_forward2_linear2_weight_quantized, x = input_375)[name = tensor<string, []>("linear_63")];
            tensor<fp32, []> var_1425 = const()[name = tensor<string, []>("op_1425"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1426 = mul(x = input_379, y = var_1425)[name = tensor<string, []>("op_1426")];
            tensor<fp32, [1, 188, 1024]> input_381 = add(x = input_369, y = var_1426)[name = tensor<string, []>("input_381")];
            tensor<int32, [1]> input_383_axes_0 = const()[name = tensor<string, []>("input_383_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_383 = layer_norm(axes = input_383_axes_0, beta = encoder_layers_6_norm_out_bias, epsilon = var_11, gamma = encoder_layers_6_norm_out_weight, x = input_381)[name = tensor<string, []>("input_383")];
            tensor<int32, [1]> input_385_axes_0 = const()[name = tensor<string, []>("input_385_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_385 = layer_norm(axes = input_385_axes_0, beta = encoder_layers_7_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_7_norm_feed_forward1_weight, x = input_383)[name = tensor<string, []>("input_385")];
            tensor<fp32, [1, 188, 4096]> input_387 = linear(bias = encoder_layers_7_feed_forward1_linear1_bias, weight = encoder_layers_7_feed_forward1_linear1_weight_quantized, x = input_385)[name = tensor<string, []>("linear_64")];
            tensor<fp32, [1, 188, 4096]> input_389 = silu(x = input_387)[name = tensor<string, []>("input_389")];
            tensor<fp32, [1, 188, 1024]> input_393 = linear(bias = encoder_layers_7_feed_forward1_linear2_bias, weight = encoder_layers_7_feed_forward1_linear2_weight_quantized, x = input_389)[name = tensor<string, []>("linear_65")];
            tensor<fp32, []> var_1456 = const()[name = tensor<string, []>("op_1456"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1457 = mul(x = input_393, y = var_1456)[name = tensor<string, []>("op_1457")];
            tensor<fp32, [1, 188, 1024]> input_395 = add(x = input_383, y = var_1457)[name = tensor<string, []>("input_395")];
            tensor<int32, [1]> query_15_axes_0 = const()[name = tensor<string, []>("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_15 = layer_norm(axes = query_15_axes_0, beta = encoder_layers_7_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_7_norm_self_att_weight, x = input_395)[name = tensor<string, []>("query_15")];
            tensor<fp32, [1, 188, 1024]> var_1473 = linear(bias = encoder_layers_7_self_attn_linear_q_bias, weight = encoder_layers_7_self_attn_linear_q_weight_quantized, x = query_15)[name = tensor<string, []>("linear_66")];
            tensor<int32, [4]> var_1474 = const()[name = tensor<string, []>("op_1474"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_43 = reshape(shape = var_1474, x = var_1473)[name = tensor<string, []>("q_43")];
            tensor<fp32, [1, 188, 1024]> var_1478 = linear(bias = encoder_layers_7_self_attn_linear_k_bias, weight = encoder_layers_7_self_attn_linear_k_weight_quantized, x = query_15)[name = tensor<string, []>("linear_67")];
            tensor<int32, [4]> var_1479 = const()[name = tensor<string, []>("op_1479"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_29 = reshape(shape = var_1479, x = var_1478)[name = tensor<string, []>("k_29")];
            tensor<fp32, [1, 188, 1024]> var_1483 = linear(bias = encoder_layers_7_self_attn_linear_v_bias, weight = encoder_layers_7_self_attn_linear_v_weight_quantized, x = query_15)[name = tensor<string, []>("linear_68")];
            tensor<int32, [4]> var_1484 = const()[name = tensor<string, []>("op_1484"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_15 = reshape(shape = var_1484, x = var_1483)[name = tensor<string, []>("v_15")];
            tensor<int32, [4]> value_17_perm_0 = const()[name = tensor<string, []>("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_1496 = add(x = q_43, y = encoder_layers_7_self_attn_pos_bias_u)[name = tensor<string, []>("op_1496")];
            tensor<fp32, [1, 188, 8, 128]> var_1498 = add(x = q_43, y = encoder_layers_7_self_attn_pos_bias_v)[name = tensor<string, []>("op_1498")];
            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_1500_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1500_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590508224))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590892736))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590892288)))];
            tensor<bool, []> x_163_transpose_x_0 = const()[name = tensor<string, []>("x_163_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_163_transpose_y_0 = const()[name = tensor<string, []>("x_163_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_15 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1498)[name = tensor<string, []>("transpose_262")];
            tensor<fp32, [1, 8, 188, 375]> x_163 = matmul(transpose_x = x_163_transpose_x_0, transpose_y = x_163_transpose_y_0, x = q_with_bias_v_15, y = op_1500_quantized)[name = tensor<string, []>("x_163")];
            tensor<fp32, []> const_84 = const()[name = tensor<string, []>("const_84"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_165_pad_0 = const()[name = tensor<string, []>("x_165_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_165_mode_0 = const()[name = tensor<string, []>("x_165_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_165 = pad(constant_val = const_84, mode = x_165_mode_0, pad = x_165_pad_0, x = x_163)[name = tensor<string, []>("x_165")];
            tensor<int32, [4]> var_1508 = const()[name = tensor<string, []>("op_1508"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_167 = reshape(shape = var_1508, x = x_165)[name = tensor<string, []>("x_167")];
            tensor<int32, [4]> var_1512_begin_0 = const()[name = tensor<string, []>("op_1512_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1512_end_0 = const()[name = tensor<string, []>("op_1512_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1512_end_mask_0 = const()[name = tensor<string, []>("op_1512_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_1512 = slice_by_index(begin = var_1512_begin_0, end = var_1512_end_0, end_mask = var_1512_end_mask_0, x = x_167)[name = tensor<string, []>("op_1512")];
            tensor<int32, [4]> var_1513 = const()[name = tensor<string, []>("op_1513"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_29 = reshape(shape = var_1513, x = var_1512)[name = tensor<string, []>("matrix_bd_29")];
            tensor<bool, []> matrix_ac_15_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_15_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_15_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_15_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_110_perm_0 = const()[name = tensor<string, []>("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_111_perm_0 = const()[name = tensor<string, []>("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29)[name = tensor<string, []>("transpose_260")];
            tensor<fp32, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1496)[name = tensor<string, []>("transpose_261")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_15 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = tensor<string, []>("matrix_ac_15")];
            tensor<int32, [4]> matrix_bd_31_begin_0 = const()[name = tensor<string, []>("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = tensor<string, []>("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_31 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29)[name = tensor<string, []>("matrix_bd_31")];
            tensor<fp32, [1, 8, 188, 188]> var_1522 = add(x = matrix_ac_15, y = matrix_bd_31)[name = tensor<string, []>("op_1522")];
            tensor<fp32, []> _inversed_scores_29_y_0 = const()[name = tensor<string, []>("_inversed_scores_29_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_29 = mul(x = var_1522, y = _inversed_scores_29_y_0)[name = tensor<string, []>("_inversed_scores_29")];
            tensor<fp32, [1, 8, 188, 188]> scores_31 = select(a = var_14, b = _inversed_scores_29, cond = mask_3)[name = tensor<string, []>("scores_31")];
            tensor<fp32, [1, 8, 188, 188]> var_1528 = softmax(axis = var_32, x = scores_31)[name = tensor<string, []>("op_1528")];
            tensor<fp32, [1, 8, 188, 188]> input_397 = select(a = var_13, b = var_1528, cond = mask_3)[name = tensor<string, []>("input_397")];
            tensor<bool, []> x_169_transpose_x_0 = const()[name = tensor<string, []>("x_169_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_169_transpose_y_0 = const()[name = tensor<string, []>("x_169_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_17 = transpose(perm = value_17_perm_0, x = v_15)[name = tensor<string, []>("transpose_259")];
            tensor<fp32, [1, 8, 188, 128]> x_169 = matmul(transpose_x = x_169_transpose_x_0, transpose_y = x_169_transpose_y_0, x = input_397, y = value_17)[name = tensor<string, []>("x_169")];
            tensor<int32, [4]> var_1532_perm_0 = const()[name = tensor<string, []>("op_1532_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_1532 = transpose(perm = var_1532_perm_0, x = x_169)[name = tensor<string, []>("transpose_258")];
            tensor<fp32, [1, 188, 1024]> input_399 = reshape(shape = var_1533, x = var_1532)[name = tensor<string, []>("input_399")];
            tensor<fp32, [1, 188, 1024]> input_401 = linear(bias = encoder_layers_7_self_attn_linear_out_bias, weight = encoder_layers_7_self_attn_linear_out_weight_quantized, x = input_399)[name = tensor<string, []>("linear_70")];
            tensor<fp32, [1, 188, 1024]> input_403 = add(x = input_395, y = input_401)[name = tensor<string, []>("input_403")];
            tensor<int32, [1]> x_173_axes_0 = const()[name = tensor<string, []>("x_173_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_173 = layer_norm(axes = x_173_axes_0, beta = encoder_layers_7_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_7_norm_conv_weight, x = input_403)[name = tensor<string, []>("x_173")];
            tensor<int32, [3]> input_405_perm_0 = const()[name = tensor<string, []>("input_405_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_407_pad_type_0 = const()[name = tensor<string, []>("input_407_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_407_strides_0 = const()[name = tensor<string, []>("input_407_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_407_pad_0 = const()[name = tensor<string, []>("input_407_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_407_dilations_0 = const()[name = tensor<string, []>("input_407_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_407_groups_0 = const()[name = tensor<string, []>("input_407_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_405 = transpose(perm = input_405_perm_0, x = x_173)[name = tensor<string, []>("transpose_257")];
            tensor<fp32, [1, 2048, 188]> input_407 = conv(bias = encoder_layers_7_conv_pointwise_conv1_bias, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = encoder_layers_7_conv_pointwise_conv1_weight_quantized, x = input_405)[name = tensor<string, []>("input_407")];
            tensor<int32, []> x_175_split_num_splits_0 = const()[name = tensor<string, []>("x_175_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_175_split_axis_0 = const()[name = tensor<string, []>("x_175_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_175_split_0, tensor<fp32, [1, 1024, 188]> x_175_split_1 = split(axis = x_175_split_axis_0, num_splits = x_175_split_num_splits_0, x = input_407)[name = tensor<string, []>("x_175_split")];
            tensor<fp32, [1, 1024, 188]> x_175_split_1_sigmoid = sigmoid(x = x_175_split_1)[name = tensor<string, []>("x_175_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_175 = mul(x = x_175_split_0, y = x_175_split_1_sigmoid)[name = tensor<string, []>("x_175")];
            tensor<fp32, [1, 1024, 188]> input_409 = select(a = var_13, b = x_175, cond = var_339)[name = tensor<string, []>("input_409")];
            tensor<fp32, []> const_87 = const()[name = tensor<string, []>("const_87"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_411_pad_0 = const()[name = tensor<string, []>("input_411_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_411_mode_0 = const()[name = tensor<string, []>("input_411_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_411 = pad(constant_val = const_87, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409)[name = tensor<string, []>("input_411")];
            tensor<string, []> input_413_pad_type_0 = const()[name = tensor<string, []>("input_413_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_413_groups_0 = const()[name = tensor<string, []>("input_413_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_413_strides_0 = const()[name = tensor<string, []>("input_413_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_413_pad_0 = const()[name = tensor<string, []>("input_413_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_413_dilations_0 = const()[name = tensor<string, []>("input_413_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_262_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_262_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590894336))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590904704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590903616)))];
            tensor<fp32, [1024]> const_263 = const()[name = tensor<string, []>("const_263"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590908864)))];
            tensor<fp32, [1, 1024, 188]> input_415 = conv(bias = const_263, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_quantized, x = input_411)[name = tensor<string, []>("input_415")];
            tensor<fp32, [1, 1024, 188]> input_417 = silu(x = input_415)[name = tensor<string, []>("input_417")];
            tensor<string, []> x_177_pad_type_0 = const()[name = tensor<string, []>("x_177_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_177_strides_0 = const()[name = tensor<string, []>("x_177_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_177_pad_0 = const()[name = tensor<string, []>("x_177_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_177_dilations_0 = const()[name = tensor<string, []>("x_177_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_177_groups_0 = const()[name = tensor<string, []>("x_177_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_177 = conv(bias = encoder_layers_7_conv_pointwise_conv2_bias, dilations = x_177_dilations_0, groups = x_177_groups_0, pad = x_177_pad_0, pad_type = x_177_pad_type_0, strides = x_177_strides_0, weight = encoder_layers_7_conv_pointwise_conv2_weight_quantized, x = input_417)[name = tensor<string, []>("x_177")];
            tensor<int32, [3]> input_419_perm_0 = const()[name = tensor<string, []>("input_419_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_419 = transpose(perm = input_419_perm_0, x = x_177)[name = tensor<string, []>("transpose_256")];
            tensor<fp32, [1, 188, 1024]> input_421 = add(x = input_403, y = input_419)[name = tensor<string, []>("input_421")];
            tensor<int32, [1]> input_423_axes_0 = const()[name = tensor<string, []>("input_423_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_423 = layer_norm(axes = input_423_axes_0, beta = encoder_layers_7_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_7_norm_feed_forward2_weight, x = input_421)[name = tensor<string, []>("input_423")];
            tensor<fp32, [1, 188, 4096]> input_425 = linear(bias = encoder_layers_7_feed_forward2_linear1_bias, weight = encoder_layers_7_feed_forward2_linear1_weight_quantized, x = input_423)[name = tensor<string, []>("linear_71")];
            tensor<fp32, [1, 188, 4096]> input_427 = silu(x = input_425)[name = tensor<string, []>("input_427")];
            tensor<fp32, [1, 188, 1024]> input_431 = linear(bias = encoder_layers_7_feed_forward2_linear2_bias, weight = encoder_layers_7_feed_forward2_linear2_weight_quantized, x = input_427)[name = tensor<string, []>("linear_72")];
            tensor<fp32, []> var_1599 = const()[name = tensor<string, []>("op_1599"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1600 = mul(x = input_431, y = var_1599)[name = tensor<string, []>("op_1600")];
            tensor<fp32, [1, 188, 1024]> input_433 = add(x = input_421, y = var_1600)[name = tensor<string, []>("input_433")];
            tensor<int32, [1]> input_435_axes_0 = const()[name = tensor<string, []>("input_435_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_435 = layer_norm(axes = input_435_axes_0, beta = encoder_layers_7_norm_out_bias, epsilon = var_11, gamma = encoder_layers_7_norm_out_weight, x = input_433)[name = tensor<string, []>("input_435")];
            tensor<int32, [1]> input_437_axes_0 = const()[name = tensor<string, []>("input_437_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_437 = layer_norm(axes = input_437_axes_0, beta = encoder_layers_8_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_8_norm_feed_forward1_weight, x = input_435)[name = tensor<string, []>("input_437")];
            tensor<fp32, [1, 188, 4096]> input_439 = linear(bias = encoder_layers_8_feed_forward1_linear1_bias, weight = encoder_layers_8_feed_forward1_linear1_weight_quantized, x = input_437)[name = tensor<string, []>("linear_73")];
            tensor<fp32, [1, 188, 4096]> input_441 = silu(x = input_439)[name = tensor<string, []>("input_441")];
            tensor<fp32, [1, 188, 1024]> input_445 = linear(bias = encoder_layers_8_feed_forward1_linear2_bias, weight = encoder_layers_8_feed_forward1_linear2_weight_quantized, x = input_441)[name = tensor<string, []>("linear_74")];
            tensor<fp32, []> var_1630 = const()[name = tensor<string, []>("op_1630"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1631 = mul(x = input_445, y = var_1630)[name = tensor<string, []>("op_1631")];
            tensor<fp32, [1, 188, 1024]> input_447 = add(x = input_435, y = var_1631)[name = tensor<string, []>("input_447")];
            tensor<int32, [1]> query_17_axes_0 = const()[name = tensor<string, []>("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_17 = layer_norm(axes = query_17_axes_0, beta = encoder_layers_8_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_8_norm_self_att_weight, x = input_447)[name = tensor<string, []>("query_17")];
            tensor<fp32, [1, 188, 1024]> var_1647 = linear(bias = encoder_layers_8_self_attn_linear_q_bias, weight = encoder_layers_8_self_attn_linear_q_weight_quantized, x = query_17)[name = tensor<string, []>("linear_75")];
            tensor<int32, [4]> var_1648 = const()[name = tensor<string, []>("op_1648"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_49 = reshape(shape = var_1648, x = var_1647)[name = tensor<string, []>("q_49")];
            tensor<fp32, [1, 188, 1024]> var_1652 = linear(bias = encoder_layers_8_self_attn_linear_k_bias, weight = encoder_layers_8_self_attn_linear_k_weight_quantized, x = query_17)[name = tensor<string, []>("linear_76")];
            tensor<int32, [4]> var_1653 = const()[name = tensor<string, []>("op_1653"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_33 = reshape(shape = var_1653, x = var_1652)[name = tensor<string, []>("k_33")];
            tensor<fp32, [1, 188, 1024]> var_1657 = linear(bias = encoder_layers_8_self_attn_linear_v_bias, weight = encoder_layers_8_self_attn_linear_v_weight_quantized, x = query_17)[name = tensor<string, []>("linear_77")];
            tensor<int32, [4]> var_1658 = const()[name = tensor<string, []>("op_1658"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_17 = reshape(shape = var_1658, x = var_1657)[name = tensor<string, []>("v_17")];
            tensor<int32, [4]> value_19_perm_0 = const()[name = tensor<string, []>("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_1670 = add(x = q_49, y = encoder_layers_8_self_attn_pos_bias_u)[name = tensor<string, []>("op_1670")];
            tensor<fp32, [1, 188, 8, 128]> var_1672 = add(x = q_49, y = encoder_layers_8_self_attn_pos_bias_v)[name = tensor<string, []>("op_1672")];
            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_1674_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1674_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(590913024))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591297536))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591297088)))];
            tensor<bool, []> x_185_transpose_x_0 = const()[name = tensor<string, []>("x_185_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_185_transpose_y_0 = const()[name = tensor<string, []>("x_185_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_17 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1672)[name = tensor<string, []>("transpose_255")];
            tensor<fp32, [1, 8, 188, 375]> x_185 = matmul(transpose_x = x_185_transpose_x_0, transpose_y = x_185_transpose_y_0, x = q_with_bias_v_17, y = op_1674_quantized)[name = tensor<string, []>("x_185")];
            tensor<fp32, []> const_94 = const()[name = tensor<string, []>("const_94"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_187_pad_0 = const()[name = tensor<string, []>("x_187_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_187_mode_0 = const()[name = tensor<string, []>("x_187_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_187 = pad(constant_val = const_94, mode = x_187_mode_0, pad = x_187_pad_0, x = x_185)[name = tensor<string, []>("x_187")];
            tensor<int32, [4]> var_1682 = const()[name = tensor<string, []>("op_1682"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_189 = reshape(shape = var_1682, x = x_187)[name = tensor<string, []>("x_189")];
            tensor<int32, [4]> var_1686_begin_0 = const()[name = tensor<string, []>("op_1686_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1686_end_0 = const()[name = tensor<string, []>("op_1686_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1686_end_mask_0 = const()[name = tensor<string, []>("op_1686_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_1686 = slice_by_index(begin = var_1686_begin_0, end = var_1686_end_0, end_mask = var_1686_end_mask_0, x = x_189)[name = tensor<string, []>("op_1686")];
            tensor<int32, [4]> var_1687 = const()[name = tensor<string, []>("op_1687"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_33 = reshape(shape = var_1687, x = var_1686)[name = tensor<string, []>("matrix_bd_33")];
            tensor<bool, []> matrix_ac_17_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_17_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_17_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_17_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_112_perm_0 = const()[name = tensor<string, []>("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_113_perm_0 = const()[name = tensor<string, []>("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33)[name = tensor<string, []>("transpose_253")];
            tensor<fp32, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1670)[name = tensor<string, []>("transpose_254")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_17 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = tensor<string, []>("matrix_ac_17")];
            tensor<int32, [4]> matrix_bd_35_begin_0 = const()[name = tensor<string, []>("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = tensor<string, []>("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_35 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33)[name = tensor<string, []>("matrix_bd_35")];
            tensor<fp32, [1, 8, 188, 188]> var_1696 = add(x = matrix_ac_17, y = matrix_bd_35)[name = tensor<string, []>("op_1696")];
            tensor<fp32, []> _inversed_scores_33_y_0 = const()[name = tensor<string, []>("_inversed_scores_33_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_33 = mul(x = var_1696, y = _inversed_scores_33_y_0)[name = tensor<string, []>("_inversed_scores_33")];
            tensor<fp32, [1, 8, 188, 188]> scores_35 = select(a = var_14, b = _inversed_scores_33, cond = mask_3)[name = tensor<string, []>("scores_35")];
            tensor<fp32, [1, 8, 188, 188]> var_1702 = softmax(axis = var_32, x = scores_35)[name = tensor<string, []>("op_1702")];
            tensor<fp32, [1, 8, 188, 188]> input_449 = select(a = var_13, b = var_1702, cond = mask_3)[name = tensor<string, []>("input_449")];
            tensor<bool, []> x_191_transpose_x_0 = const()[name = tensor<string, []>("x_191_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_191_transpose_y_0 = const()[name = tensor<string, []>("x_191_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_19 = transpose(perm = value_19_perm_0, x = v_17)[name = tensor<string, []>("transpose_252")];
            tensor<fp32, [1, 8, 188, 128]> x_191 = matmul(transpose_x = x_191_transpose_x_0, transpose_y = x_191_transpose_y_0, x = input_449, y = value_19)[name = tensor<string, []>("x_191")];
            tensor<int32, [4]> var_1706_perm_0 = const()[name = tensor<string, []>("op_1706_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1707 = const()[name = tensor<string, []>("op_1707"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_1706 = transpose(perm = var_1706_perm_0, x = x_191)[name = tensor<string, []>("transpose_251")];
            tensor<fp32, [1, 188, 1024]> input_451 = reshape(shape = var_1707, x = var_1706)[name = tensor<string, []>("input_451")];
            tensor<fp32, [1, 188, 1024]> input_453 = linear(bias = encoder_layers_8_self_attn_linear_out_bias, weight = encoder_layers_8_self_attn_linear_out_weight_quantized, x = input_451)[name = tensor<string, []>("linear_79")];
            tensor<fp32, [1, 188, 1024]> input_455 = add(x = input_447, y = input_453)[name = tensor<string, []>("input_455")];
            tensor<int32, [1]> x_195_axes_0 = const()[name = tensor<string, []>("x_195_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_195 = layer_norm(axes = x_195_axes_0, beta = encoder_layers_8_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_8_norm_conv_weight, x = input_455)[name = tensor<string, []>("x_195")];
            tensor<int32, [3]> input_457_perm_0 = const()[name = tensor<string, []>("input_457_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_459_pad_type_0 = const()[name = tensor<string, []>("input_459_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_459_strides_0 = const()[name = tensor<string, []>("input_459_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_459_pad_0 = const()[name = tensor<string, []>("input_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_459_dilations_0 = const()[name = tensor<string, []>("input_459_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_459_groups_0 = const()[name = tensor<string, []>("input_459_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_457 = transpose(perm = input_457_perm_0, x = x_195)[name = tensor<string, []>("transpose_250")];
            tensor<fp32, [1, 2048, 188]> input_459 = conv(bias = encoder_layers_8_conv_pointwise_conv1_bias, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = encoder_layers_8_conv_pointwise_conv1_weight_quantized, x = input_457)[name = tensor<string, []>("input_459")];
            tensor<int32, []> x_197_split_num_splits_0 = const()[name = tensor<string, []>("x_197_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_197_split_axis_0 = const()[name = tensor<string, []>("x_197_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_197_split_0, tensor<fp32, [1, 1024, 188]> x_197_split_1 = split(axis = x_197_split_axis_0, num_splits = x_197_split_num_splits_0, x = input_459)[name = tensor<string, []>("x_197_split")];
            tensor<fp32, [1, 1024, 188]> x_197_split_1_sigmoid = sigmoid(x = x_197_split_1)[name = tensor<string, []>("x_197_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_197 = mul(x = x_197_split_0, y = x_197_split_1_sigmoid)[name = tensor<string, []>("x_197")];
            tensor<fp32, [1, 1024, 188]> input_461 = select(a = var_13, b = x_197, cond = var_339)[name = tensor<string, []>("input_461")];
            tensor<fp32, []> const_97 = const()[name = tensor<string, []>("const_97"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_463_pad_0 = const()[name = tensor<string, []>("input_463_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_463_mode_0 = const()[name = tensor<string, []>("input_463_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_463 = pad(constant_val = const_97, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461)[name = tensor<string, []>("input_463")];
            tensor<string, []> input_465_pad_type_0 = const()[name = tensor<string, []>("input_465_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_465_groups_0 = const()[name = tensor<string, []>("input_465_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_465_strides_0 = const()[name = tensor<string, []>("input_465_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_465_pad_0 = const()[name = tensor<string, []>("input_465_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_465_dilations_0 = const()[name = tensor<string, []>("input_465_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_264_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_264_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591299136))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591309504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591308416)))];
            tensor<fp32, [1024]> const_265 = const()[name = tensor<string, []>("const_265"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591313664)))];
            tensor<fp32, [1, 1024, 188]> input_467 = conv(bias = const_265, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_quantized, x = input_463)[name = tensor<string, []>("input_467")];
            tensor<fp32, [1, 1024, 188]> input_469 = silu(x = input_467)[name = tensor<string, []>("input_469")];
            tensor<string, []> x_199_pad_type_0 = const()[name = tensor<string, []>("x_199_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_199_strides_0 = const()[name = tensor<string, []>("x_199_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_199_pad_0 = const()[name = tensor<string, []>("x_199_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_199_dilations_0 = const()[name = tensor<string, []>("x_199_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_199_groups_0 = const()[name = tensor<string, []>("x_199_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_199 = conv(bias = encoder_layers_8_conv_pointwise_conv2_bias, dilations = x_199_dilations_0, groups = x_199_groups_0, pad = x_199_pad_0, pad_type = x_199_pad_type_0, strides = x_199_strides_0, weight = encoder_layers_8_conv_pointwise_conv2_weight_quantized, x = input_469)[name = tensor<string, []>("x_199")];
            tensor<int32, [3]> input_471_perm_0 = const()[name = tensor<string, []>("input_471_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_471 = transpose(perm = input_471_perm_0, x = x_199)[name = tensor<string, []>("transpose_249")];
            tensor<fp32, [1, 188, 1024]> input_473 = add(x = input_455, y = input_471)[name = tensor<string, []>("input_473")];
            tensor<int32, [1]> input_475_axes_0 = const()[name = tensor<string, []>("input_475_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_475 = layer_norm(axes = input_475_axes_0, beta = encoder_layers_8_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_8_norm_feed_forward2_weight, x = input_473)[name = tensor<string, []>("input_475")];
            tensor<fp32, [1, 188, 4096]> input_477 = linear(bias = encoder_layers_8_feed_forward2_linear1_bias, weight = encoder_layers_8_feed_forward2_linear1_weight_quantized, x = input_475)[name = tensor<string, []>("linear_80")];
            tensor<fp32, [1, 188, 4096]> input_479 = silu(x = input_477)[name = tensor<string, []>("input_479")];
            tensor<fp32, [1, 188, 1024]> input_483 = linear(bias = encoder_layers_8_feed_forward2_linear2_bias, weight = encoder_layers_8_feed_forward2_linear2_weight_quantized, x = input_479)[name = tensor<string, []>("linear_81")];
            tensor<fp32, []> var_1773 = const()[name = tensor<string, []>("op_1773"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1774 = mul(x = input_483, y = var_1773)[name = tensor<string, []>("op_1774")];
            tensor<fp32, [1, 188, 1024]> input_485 = add(x = input_473, y = var_1774)[name = tensor<string, []>("input_485")];
            tensor<int32, [1]> input_487_axes_0 = const()[name = tensor<string, []>("input_487_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_487 = layer_norm(axes = input_487_axes_0, beta = encoder_layers_8_norm_out_bias, epsilon = var_11, gamma = encoder_layers_8_norm_out_weight, x = input_485)[name = tensor<string, []>("input_487")];
            tensor<int32, [1]> input_489_axes_0 = const()[name = tensor<string, []>("input_489_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_489 = layer_norm(axes = input_489_axes_0, beta = encoder_layers_9_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_9_norm_feed_forward1_weight, x = input_487)[name = tensor<string, []>("input_489")];
            tensor<fp32, [1, 188, 4096]> input_491 = linear(bias = encoder_layers_9_feed_forward1_linear1_bias, weight = encoder_layers_9_feed_forward1_linear1_weight_quantized, x = input_489)[name = tensor<string, []>("linear_82")];
            tensor<fp32, [1, 188, 4096]> input_493 = silu(x = input_491)[name = tensor<string, []>("input_493")];
            tensor<fp32, [1, 188, 1024]> input_497 = linear(bias = encoder_layers_9_feed_forward1_linear2_bias, weight = encoder_layers_9_feed_forward1_linear2_weight_quantized, x = input_493)[name = tensor<string, []>("linear_83")];
            tensor<fp32, []> var_1804 = const()[name = tensor<string, []>("op_1804"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1805 = mul(x = input_497, y = var_1804)[name = tensor<string, []>("op_1805")];
            tensor<fp32, [1, 188, 1024]> input_499 = add(x = input_487, y = var_1805)[name = tensor<string, []>("input_499")];
            tensor<int32, [1]> query_19_axes_0 = const()[name = tensor<string, []>("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_19 = layer_norm(axes = query_19_axes_0, beta = encoder_layers_9_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_9_norm_self_att_weight, x = input_499)[name = tensor<string, []>("query_19")];
            tensor<fp32, [1, 188, 1024]> var_1821 = linear(bias = encoder_layers_9_self_attn_linear_q_bias, weight = encoder_layers_9_self_attn_linear_q_weight_quantized, x = query_19)[name = tensor<string, []>("linear_84")];
            tensor<int32, [4]> var_1822 = const()[name = tensor<string, []>("op_1822"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_55 = reshape(shape = var_1822, x = var_1821)[name = tensor<string, []>("q_55")];
            tensor<fp32, [1, 188, 1024]> var_1826 = linear(bias = encoder_layers_9_self_attn_linear_k_bias, weight = encoder_layers_9_self_attn_linear_k_weight_quantized, x = query_19)[name = tensor<string, []>("linear_85")];
            tensor<int32, [4]> var_1827 = const()[name = tensor<string, []>("op_1827"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_37 = reshape(shape = var_1827, x = var_1826)[name = tensor<string, []>("k_37")];
            tensor<fp32, [1, 188, 1024]> var_1831 = linear(bias = encoder_layers_9_self_attn_linear_v_bias, weight = encoder_layers_9_self_attn_linear_v_weight_quantized, x = query_19)[name = tensor<string, []>("linear_86")];
            tensor<int32, [4]> var_1832 = const()[name = tensor<string, []>("op_1832"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_19 = reshape(shape = var_1832, x = var_1831)[name = tensor<string, []>("v_19")];
            tensor<int32, [4]> value_21_perm_0 = const()[name = tensor<string, []>("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_1844 = add(x = q_55, y = encoder_layers_9_self_attn_pos_bias_u)[name = tensor<string, []>("op_1844")];
            tensor<fp32, [1, 188, 8, 128]> var_1846 = add(x = q_55, y = encoder_layers_9_self_attn_pos_bias_v)[name = tensor<string, []>("op_1846")];
            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_1848_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1848_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591317824))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591702336))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591701888)))];
            tensor<bool, []> x_207_transpose_x_0 = const()[name = tensor<string, []>("x_207_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_207_transpose_y_0 = const()[name = tensor<string, []>("x_207_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_19 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1846)[name = tensor<string, []>("transpose_248")];
            tensor<fp32, [1, 8, 188, 375]> x_207 = matmul(transpose_x = x_207_transpose_x_0, transpose_y = x_207_transpose_y_0, x = q_with_bias_v_19, y = op_1848_quantized)[name = tensor<string, []>("x_207")];
            tensor<fp32, []> const_104 = const()[name = tensor<string, []>("const_104"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_209_pad_0 = const()[name = tensor<string, []>("x_209_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_209_mode_0 = const()[name = tensor<string, []>("x_209_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_209 = pad(constant_val = const_104, mode = x_209_mode_0, pad = x_209_pad_0, x = x_207)[name = tensor<string, []>("x_209")];
            tensor<int32, [4]> var_1856 = const()[name = tensor<string, []>("op_1856"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_211 = reshape(shape = var_1856, x = x_209)[name = tensor<string, []>("x_211")];
            tensor<int32, [4]> var_1860_begin_0 = const()[name = tensor<string, []>("op_1860_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1860_end_0 = const()[name = tensor<string, []>("op_1860_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1860_end_mask_0 = const()[name = tensor<string, []>("op_1860_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_1860 = slice_by_index(begin = var_1860_begin_0, end = var_1860_end_0, end_mask = var_1860_end_mask_0, x = x_211)[name = tensor<string, []>("op_1860")];
            tensor<int32, [4]> var_1861 = const()[name = tensor<string, []>("op_1861"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_37 = reshape(shape = var_1861, x = var_1860)[name = tensor<string, []>("matrix_bd_37")];
            tensor<bool, []> matrix_ac_19_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_19_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_114_perm_0 = const()[name = tensor<string, []>("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_115_perm_0 = const()[name = tensor<string, []>("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37)[name = tensor<string, []>("transpose_246")];
            tensor<fp32, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1844)[name = tensor<string, []>("transpose_247")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_19 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = tensor<string, []>("matrix_ac_19")];
            tensor<int32, [4]> matrix_bd_39_begin_0 = const()[name = tensor<string, []>("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = tensor<string, []>("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_39 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37)[name = tensor<string, []>("matrix_bd_39")];
            tensor<fp32, [1, 8, 188, 188]> var_1870 = add(x = matrix_ac_19, y = matrix_bd_39)[name = tensor<string, []>("op_1870")];
            tensor<fp32, []> _inversed_scores_37_y_0 = const()[name = tensor<string, []>("_inversed_scores_37_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_37 = mul(x = var_1870, y = _inversed_scores_37_y_0)[name = tensor<string, []>("_inversed_scores_37")];
            tensor<fp32, [1, 8, 188, 188]> scores_39 = select(a = var_14, b = _inversed_scores_37, cond = mask_3)[name = tensor<string, []>("scores_39")];
            tensor<fp32, [1, 8, 188, 188]> var_1876 = softmax(axis = var_32, x = scores_39)[name = tensor<string, []>("op_1876")];
            tensor<fp32, [1, 8, 188, 188]> input_501 = select(a = var_13, b = var_1876, cond = mask_3)[name = tensor<string, []>("input_501")];
            tensor<bool, []> x_213_transpose_x_0 = const()[name = tensor<string, []>("x_213_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_213_transpose_y_0 = const()[name = tensor<string, []>("x_213_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_21 = transpose(perm = value_21_perm_0, x = v_19)[name = tensor<string, []>("transpose_245")];
            tensor<fp32, [1, 8, 188, 128]> x_213 = matmul(transpose_x = x_213_transpose_x_0, transpose_y = x_213_transpose_y_0, x = input_501, y = value_21)[name = tensor<string, []>("x_213")];
            tensor<int32, [4]> var_1880_perm_0 = const()[name = tensor<string, []>("op_1880_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1881 = const()[name = tensor<string, []>("op_1881"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_1880 = transpose(perm = var_1880_perm_0, x = x_213)[name = tensor<string, []>("transpose_244")];
            tensor<fp32, [1, 188, 1024]> input_503 = reshape(shape = var_1881, x = var_1880)[name = tensor<string, []>("input_503")];
            tensor<fp32, [1, 188, 1024]> input_505 = linear(bias = encoder_layers_9_self_attn_linear_out_bias, weight = encoder_layers_9_self_attn_linear_out_weight_quantized, x = input_503)[name = tensor<string, []>("linear_88")];
            tensor<fp32, [1, 188, 1024]> input_507 = add(x = input_499, y = input_505)[name = tensor<string, []>("input_507")];
            tensor<int32, [1]> x_217_axes_0 = const()[name = tensor<string, []>("x_217_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_217 = layer_norm(axes = x_217_axes_0, beta = encoder_layers_9_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_9_norm_conv_weight, x = input_507)[name = tensor<string, []>("x_217")];
            tensor<int32, [3]> input_509_perm_0 = const()[name = tensor<string, []>("input_509_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_511_pad_type_0 = const()[name = tensor<string, []>("input_511_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_511_strides_0 = const()[name = tensor<string, []>("input_511_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_511_pad_0 = const()[name = tensor<string, []>("input_511_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_511_dilations_0 = const()[name = tensor<string, []>("input_511_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_511_groups_0 = const()[name = tensor<string, []>("input_511_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_509 = transpose(perm = input_509_perm_0, x = x_217)[name = tensor<string, []>("transpose_243")];
            tensor<fp32, [1, 2048, 188]> input_511 = conv(bias = encoder_layers_9_conv_pointwise_conv1_bias, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = encoder_layers_9_conv_pointwise_conv1_weight_quantized, x = input_509)[name = tensor<string, []>("input_511")];
            tensor<int32, []> x_219_split_num_splits_0 = const()[name = tensor<string, []>("x_219_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_219_split_axis_0 = const()[name = tensor<string, []>("x_219_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_219_split_0, tensor<fp32, [1, 1024, 188]> x_219_split_1 = split(axis = x_219_split_axis_0, num_splits = x_219_split_num_splits_0, x = input_511)[name = tensor<string, []>("x_219_split")];
            tensor<fp32, [1, 1024, 188]> x_219_split_1_sigmoid = sigmoid(x = x_219_split_1)[name = tensor<string, []>("x_219_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_219 = mul(x = x_219_split_0, y = x_219_split_1_sigmoid)[name = tensor<string, []>("x_219")];
            tensor<fp32, [1, 1024, 188]> input_513 = select(a = var_13, b = x_219, cond = var_339)[name = tensor<string, []>("input_513")];
            tensor<fp32, []> const_107 = const()[name = tensor<string, []>("const_107"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_515_pad_0 = const()[name = tensor<string, []>("input_515_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_515_mode_0 = const()[name = tensor<string, []>("input_515_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_515 = pad(constant_val = const_107, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513)[name = tensor<string, []>("input_515")];
            tensor<string, []> input_517_pad_type_0 = const()[name = tensor<string, []>("input_517_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_517_groups_0 = const()[name = tensor<string, []>("input_517_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_517_strides_0 = const()[name = tensor<string, []>("input_517_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_517_pad_0 = const()[name = tensor<string, []>("input_517_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_517_dilations_0 = const()[name = tensor<string, []>("input_517_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_266_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_266_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591703936))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591714304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591713216)))];
            tensor<fp32, [1024]> const_267 = const()[name = tensor<string, []>("const_267"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591718464)))];
            tensor<fp32, [1, 1024, 188]> input_519 = conv(bias = const_267, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_quantized, x = input_515)[name = tensor<string, []>("input_519")];
            tensor<fp32, [1, 1024, 188]> input_521 = silu(x = input_519)[name = tensor<string, []>("input_521")];
            tensor<string, []> x_221_pad_type_0 = const()[name = tensor<string, []>("x_221_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_221_strides_0 = const()[name = tensor<string, []>("x_221_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_221_pad_0 = const()[name = tensor<string, []>("x_221_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_221_dilations_0 = const()[name = tensor<string, []>("x_221_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_221_groups_0 = const()[name = tensor<string, []>("x_221_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_221 = conv(bias = encoder_layers_9_conv_pointwise_conv2_bias, dilations = x_221_dilations_0, groups = x_221_groups_0, pad = x_221_pad_0, pad_type = x_221_pad_type_0, strides = x_221_strides_0, weight = encoder_layers_9_conv_pointwise_conv2_weight_quantized, x = input_521)[name = tensor<string, []>("x_221")];
            tensor<int32, [3]> input_523_perm_0 = const()[name = tensor<string, []>("input_523_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_523 = transpose(perm = input_523_perm_0, x = x_221)[name = tensor<string, []>("transpose_242")];
            tensor<fp32, [1, 188, 1024]> input_525 = add(x = input_507, y = input_523)[name = tensor<string, []>("input_525")];
            tensor<int32, [1]> input_527_axes_0 = const()[name = tensor<string, []>("input_527_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_527 = layer_norm(axes = input_527_axes_0, beta = encoder_layers_9_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_9_norm_feed_forward2_weight, x = input_525)[name = tensor<string, []>("input_527")];
            tensor<fp32, [1, 188, 4096]> input_529 = linear(bias = encoder_layers_9_feed_forward2_linear1_bias, weight = encoder_layers_9_feed_forward2_linear1_weight_quantized, x = input_527)[name = tensor<string, []>("linear_89")];
            tensor<fp32, [1, 188, 4096]> input_531 = silu(x = input_529)[name = tensor<string, []>("input_531")];
            tensor<fp32, [1, 188, 1024]> input_535 = linear(bias = encoder_layers_9_feed_forward2_linear2_bias, weight = encoder_layers_9_feed_forward2_linear2_weight_quantized, x = input_531)[name = tensor<string, []>("linear_90")];
            tensor<fp32, []> var_1947 = const()[name = tensor<string, []>("op_1947"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1948 = mul(x = input_535, y = var_1947)[name = tensor<string, []>("op_1948")];
            tensor<fp32, [1, 188, 1024]> input_537 = add(x = input_525, y = var_1948)[name = tensor<string, []>("input_537")];
            tensor<int32, [1]> input_539_axes_0 = const()[name = tensor<string, []>("input_539_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_539 = layer_norm(axes = input_539_axes_0, beta = encoder_layers_9_norm_out_bias, epsilon = var_11, gamma = encoder_layers_9_norm_out_weight, x = input_537)[name = tensor<string, []>("input_539")];
            tensor<int32, [1]> input_541_axes_0 = const()[name = tensor<string, []>("input_541_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_541 = layer_norm(axes = input_541_axes_0, beta = encoder_layers_10_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_10_norm_feed_forward1_weight, x = input_539)[name = tensor<string, []>("input_541")];
            tensor<fp32, [1, 188, 4096]> input_543 = linear(bias = encoder_layers_10_feed_forward1_linear1_bias, weight = encoder_layers_10_feed_forward1_linear1_weight_quantized, x = input_541)[name = tensor<string, []>("linear_91")];
            tensor<fp32, [1, 188, 4096]> input_545 = silu(x = input_543)[name = tensor<string, []>("input_545")];
            tensor<fp32, [1, 188, 1024]> input_549 = linear(bias = encoder_layers_10_feed_forward1_linear2_bias, weight = encoder_layers_10_feed_forward1_linear2_weight_quantized, x = input_545)[name = tensor<string, []>("linear_92")];
            tensor<fp32, []> var_1978 = const()[name = tensor<string, []>("op_1978"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_1979 = mul(x = input_549, y = var_1978)[name = tensor<string, []>("op_1979")];
            tensor<fp32, [1, 188, 1024]> input_551 = add(x = input_539, y = var_1979)[name = tensor<string, []>("input_551")];
            tensor<int32, [1]> query_21_axes_0 = const()[name = tensor<string, []>("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_21 = layer_norm(axes = query_21_axes_0, beta = encoder_layers_10_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_10_norm_self_att_weight, x = input_551)[name = tensor<string, []>("query_21")];
            tensor<fp32, [1, 188, 1024]> var_1995 = linear(bias = encoder_layers_10_self_attn_linear_q_bias, weight = encoder_layers_10_self_attn_linear_q_weight_quantized, x = query_21)[name = tensor<string, []>("linear_93")];
            tensor<int32, [4]> var_1996 = const()[name = tensor<string, []>("op_1996"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_61 = reshape(shape = var_1996, x = var_1995)[name = tensor<string, []>("q_61")];
            tensor<fp32, [1, 188, 1024]> var_2000 = linear(bias = encoder_layers_10_self_attn_linear_k_bias, weight = encoder_layers_10_self_attn_linear_k_weight_quantized, x = query_21)[name = tensor<string, []>("linear_94")];
            tensor<int32, [4]> var_2001 = const()[name = tensor<string, []>("op_2001"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_41 = reshape(shape = var_2001, x = var_2000)[name = tensor<string, []>("k_41")];
            tensor<fp32, [1, 188, 1024]> var_2005 = linear(bias = encoder_layers_10_self_attn_linear_v_bias, weight = encoder_layers_10_self_attn_linear_v_weight_quantized, x = query_21)[name = tensor<string, []>("linear_95")];
            tensor<int32, [4]> var_2006 = const()[name = tensor<string, []>("op_2006"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_21 = reshape(shape = var_2006, x = var_2005)[name = tensor<string, []>("v_21")];
            tensor<int32, [4]> value_23_perm_0 = const()[name = tensor<string, []>("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_2018 = add(x = q_61, y = encoder_layers_10_self_attn_pos_bias_u)[name = tensor<string, []>("op_2018")];
            tensor<fp32, [1, 188, 8, 128]> var_2020 = add(x = q_61, y = encoder_layers_10_self_attn_pos_bias_v)[name = tensor<string, []>("op_2020")];
            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_2022_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2022_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591722624))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592107136))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592106688)))];
            tensor<bool, []> x_229_transpose_x_0 = const()[name = tensor<string, []>("x_229_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_229_transpose_y_0 = const()[name = tensor<string, []>("x_229_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_21 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2020)[name = tensor<string, []>("transpose_241")];
            tensor<fp32, [1, 8, 188, 375]> x_229 = matmul(transpose_x = x_229_transpose_x_0, transpose_y = x_229_transpose_y_0, x = q_with_bias_v_21, y = op_2022_quantized)[name = tensor<string, []>("x_229")];
            tensor<fp32, []> const_114 = const()[name = tensor<string, []>("const_114"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_231_pad_0 = const()[name = tensor<string, []>("x_231_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_231_mode_0 = const()[name = tensor<string, []>("x_231_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_231 = pad(constant_val = const_114, mode = x_231_mode_0, pad = x_231_pad_0, x = x_229)[name = tensor<string, []>("x_231")];
            tensor<int32, [4]> var_2030 = const()[name = tensor<string, []>("op_2030"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_233 = reshape(shape = var_2030, x = x_231)[name = tensor<string, []>("x_233")];
            tensor<int32, [4]> var_2034_begin_0 = const()[name = tensor<string, []>("op_2034_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2034_end_0 = const()[name = tensor<string, []>("op_2034_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2034_end_mask_0 = const()[name = tensor<string, []>("op_2034_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_2034 = slice_by_index(begin = var_2034_begin_0, end = var_2034_end_0, end_mask = var_2034_end_mask_0, x = x_233)[name = tensor<string, []>("op_2034")];
            tensor<int32, [4]> var_2035 = const()[name = tensor<string, []>("op_2035"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_41 = reshape(shape = var_2035, x = var_2034)[name = tensor<string, []>("matrix_bd_41")];
            tensor<bool, []> matrix_ac_21_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_21_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_21_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_21_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_116_perm_0 = const()[name = tensor<string, []>("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_117_perm_0 = const()[name = tensor<string, []>("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41)[name = tensor<string, []>("transpose_239")];
            tensor<fp32, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2018)[name = tensor<string, []>("transpose_240")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_21 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = tensor<string, []>("matrix_ac_21")];
            tensor<int32, [4]> matrix_bd_43_begin_0 = const()[name = tensor<string, []>("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = tensor<string, []>("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_43 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41)[name = tensor<string, []>("matrix_bd_43")];
            tensor<fp32, [1, 8, 188, 188]> var_2044 = add(x = matrix_ac_21, y = matrix_bd_43)[name = tensor<string, []>("op_2044")];
            tensor<fp32, []> _inversed_scores_41_y_0 = const()[name = tensor<string, []>("_inversed_scores_41_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_41 = mul(x = var_2044, y = _inversed_scores_41_y_0)[name = tensor<string, []>("_inversed_scores_41")];
            tensor<fp32, [1, 8, 188, 188]> scores_43 = select(a = var_14, b = _inversed_scores_41, cond = mask_3)[name = tensor<string, []>("scores_43")];
            tensor<fp32, [1, 8, 188, 188]> var_2050 = softmax(axis = var_32, x = scores_43)[name = tensor<string, []>("op_2050")];
            tensor<fp32, [1, 8, 188, 188]> input_553 = select(a = var_13, b = var_2050, cond = mask_3)[name = tensor<string, []>("input_553")];
            tensor<bool, []> x_235_transpose_x_0 = const()[name = tensor<string, []>("x_235_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_235_transpose_y_0 = const()[name = tensor<string, []>("x_235_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_23 = transpose(perm = value_23_perm_0, x = v_21)[name = tensor<string, []>("transpose_238")];
            tensor<fp32, [1, 8, 188, 128]> x_235 = matmul(transpose_x = x_235_transpose_x_0, transpose_y = x_235_transpose_y_0, x = input_553, y = value_23)[name = tensor<string, []>("x_235")];
            tensor<int32, [4]> var_2054_perm_0 = const()[name = tensor<string, []>("op_2054_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2055 = const()[name = tensor<string, []>("op_2055"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_2054 = transpose(perm = var_2054_perm_0, x = x_235)[name = tensor<string, []>("transpose_237")];
            tensor<fp32, [1, 188, 1024]> input_555 = reshape(shape = var_2055, x = var_2054)[name = tensor<string, []>("input_555")];
            tensor<fp32, [1, 188, 1024]> input_557 = linear(bias = encoder_layers_10_self_attn_linear_out_bias, weight = encoder_layers_10_self_attn_linear_out_weight_quantized, x = input_555)[name = tensor<string, []>("linear_97")];
            tensor<fp32, [1, 188, 1024]> input_559 = add(x = input_551, y = input_557)[name = tensor<string, []>("input_559")];
            tensor<int32, [1]> x_239_axes_0 = const()[name = tensor<string, []>("x_239_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_239 = layer_norm(axes = x_239_axes_0, beta = encoder_layers_10_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_10_norm_conv_weight, x = input_559)[name = tensor<string, []>("x_239")];
            tensor<int32, [3]> input_561_perm_0 = const()[name = tensor<string, []>("input_561_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_563_pad_type_0 = const()[name = tensor<string, []>("input_563_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_563_strides_0 = const()[name = tensor<string, []>("input_563_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_563_pad_0 = const()[name = tensor<string, []>("input_563_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_563_dilations_0 = const()[name = tensor<string, []>("input_563_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_563_groups_0 = const()[name = tensor<string, []>("input_563_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_561 = transpose(perm = input_561_perm_0, x = x_239)[name = tensor<string, []>("transpose_236")];
            tensor<fp32, [1, 2048, 188]> input_563 = conv(bias = encoder_layers_10_conv_pointwise_conv1_bias, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = encoder_layers_10_conv_pointwise_conv1_weight_quantized, x = input_561)[name = tensor<string, []>("input_563")];
            tensor<int32, []> x_241_split_num_splits_0 = const()[name = tensor<string, []>("x_241_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_241_split_axis_0 = const()[name = tensor<string, []>("x_241_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_241_split_0, tensor<fp32, [1, 1024, 188]> x_241_split_1 = split(axis = x_241_split_axis_0, num_splits = x_241_split_num_splits_0, x = input_563)[name = tensor<string, []>("x_241_split")];
            tensor<fp32, [1, 1024, 188]> x_241_split_1_sigmoid = sigmoid(x = x_241_split_1)[name = tensor<string, []>("x_241_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_241 = mul(x = x_241_split_0, y = x_241_split_1_sigmoid)[name = tensor<string, []>("x_241")];
            tensor<fp32, [1, 1024, 188]> input_565 = select(a = var_13, b = x_241, cond = var_339)[name = tensor<string, []>("input_565")];
            tensor<fp32, []> const_117 = const()[name = tensor<string, []>("const_117"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_567_pad_0 = const()[name = tensor<string, []>("input_567_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_567_mode_0 = const()[name = tensor<string, []>("input_567_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_567 = pad(constant_val = const_117, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565)[name = tensor<string, []>("input_567")];
            tensor<string, []> input_569_pad_type_0 = const()[name = tensor<string, []>("input_569_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_569_groups_0 = const()[name = tensor<string, []>("input_569_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_569_strides_0 = const()[name = tensor<string, []>("input_569_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_569_pad_0 = const()[name = tensor<string, []>("input_569_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_569_dilations_0 = const()[name = tensor<string, []>("input_569_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_268_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_268_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592108736))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592119104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592118016)))];
            tensor<fp32, [1024]> const_269 = const()[name = tensor<string, []>("const_269"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592123264)))];
            tensor<fp32, [1, 1024, 188]> input_571 = conv(bias = const_269, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_quantized, x = input_567)[name = tensor<string, []>("input_571")];
            tensor<fp32, [1, 1024, 188]> input_573 = silu(x = input_571)[name = tensor<string, []>("input_573")];
            tensor<string, []> x_243_pad_type_0 = const()[name = tensor<string, []>("x_243_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_243_strides_0 = const()[name = tensor<string, []>("x_243_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_243_pad_0 = const()[name = tensor<string, []>("x_243_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_243_dilations_0 = const()[name = tensor<string, []>("x_243_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_243_groups_0 = const()[name = tensor<string, []>("x_243_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_243 = conv(bias = encoder_layers_10_conv_pointwise_conv2_bias, dilations = x_243_dilations_0, groups = x_243_groups_0, pad = x_243_pad_0, pad_type = x_243_pad_type_0, strides = x_243_strides_0, weight = encoder_layers_10_conv_pointwise_conv2_weight_quantized, x = input_573)[name = tensor<string, []>("x_243")];
            tensor<int32, [3]> input_575_perm_0 = const()[name = tensor<string, []>("input_575_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_575 = transpose(perm = input_575_perm_0, x = x_243)[name = tensor<string, []>("transpose_235")];
            tensor<fp32, [1, 188, 1024]> input_577 = add(x = input_559, y = input_575)[name = tensor<string, []>("input_577")];
            tensor<int32, [1]> input_579_axes_0 = const()[name = tensor<string, []>("input_579_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_579 = layer_norm(axes = input_579_axes_0, beta = encoder_layers_10_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_10_norm_feed_forward2_weight, x = input_577)[name = tensor<string, []>("input_579")];
            tensor<fp32, [1, 188, 4096]> input_581 = linear(bias = encoder_layers_10_feed_forward2_linear1_bias, weight = encoder_layers_10_feed_forward2_linear1_weight_quantized, x = input_579)[name = tensor<string, []>("linear_98")];
            tensor<fp32, [1, 188, 4096]> input_583 = silu(x = input_581)[name = tensor<string, []>("input_583")];
            tensor<fp32, [1, 188, 1024]> input_587 = linear(bias = encoder_layers_10_feed_forward2_linear2_bias, weight = encoder_layers_10_feed_forward2_linear2_weight_quantized, x = input_583)[name = tensor<string, []>("linear_99")];
            tensor<fp32, []> var_2121 = const()[name = tensor<string, []>("op_2121"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2122 = mul(x = input_587, y = var_2121)[name = tensor<string, []>("op_2122")];
            tensor<fp32, [1, 188, 1024]> input_589 = add(x = input_577, y = var_2122)[name = tensor<string, []>("input_589")];
            tensor<int32, [1]> input_591_axes_0 = const()[name = tensor<string, []>("input_591_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_591 = layer_norm(axes = input_591_axes_0, beta = encoder_layers_10_norm_out_bias, epsilon = var_11, gamma = encoder_layers_10_norm_out_weight, x = input_589)[name = tensor<string, []>("input_591")];
            tensor<int32, [1]> input_593_axes_0 = const()[name = tensor<string, []>("input_593_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_593 = layer_norm(axes = input_593_axes_0, beta = encoder_layers_11_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_11_norm_feed_forward1_weight, x = input_591)[name = tensor<string, []>("input_593")];
            tensor<fp32, [1, 188, 4096]> input_595 = linear(bias = encoder_layers_11_feed_forward1_linear1_bias, weight = encoder_layers_11_feed_forward1_linear1_weight_quantized, x = input_593)[name = tensor<string, []>("linear_100")];
            tensor<fp32, [1, 188, 4096]> input_597 = silu(x = input_595)[name = tensor<string, []>("input_597")];
            tensor<fp32, [1, 188, 1024]> input_601 = linear(bias = encoder_layers_11_feed_forward1_linear2_bias, weight = encoder_layers_11_feed_forward1_linear2_weight_quantized, x = input_597)[name = tensor<string, []>("linear_101")];
            tensor<fp32, []> var_2152 = const()[name = tensor<string, []>("op_2152"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2153 = mul(x = input_601, y = var_2152)[name = tensor<string, []>("op_2153")];
            tensor<fp32, [1, 188, 1024]> input_603 = add(x = input_591, y = var_2153)[name = tensor<string, []>("input_603")];
            tensor<int32, [1]> query_23_axes_0 = const()[name = tensor<string, []>("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_23 = layer_norm(axes = query_23_axes_0, beta = encoder_layers_11_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_11_norm_self_att_weight, x = input_603)[name = tensor<string, []>("query_23")];
            tensor<fp32, [1, 188, 1024]> var_2169 = linear(bias = encoder_layers_11_self_attn_linear_q_bias, weight = encoder_layers_11_self_attn_linear_q_weight_quantized, x = query_23)[name = tensor<string, []>("linear_102")];
            tensor<int32, [4]> var_2170 = const()[name = tensor<string, []>("op_2170"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_67 = reshape(shape = var_2170, x = var_2169)[name = tensor<string, []>("q_67")];
            tensor<fp32, [1, 188, 1024]> var_2174 = linear(bias = encoder_layers_11_self_attn_linear_k_bias, weight = encoder_layers_11_self_attn_linear_k_weight_quantized, x = query_23)[name = tensor<string, []>("linear_103")];
            tensor<int32, [4]> var_2175 = const()[name = tensor<string, []>("op_2175"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_45 = reshape(shape = var_2175, x = var_2174)[name = tensor<string, []>("k_45")];
            tensor<fp32, [1, 188, 1024]> var_2179 = linear(bias = encoder_layers_11_self_attn_linear_v_bias, weight = encoder_layers_11_self_attn_linear_v_weight_quantized, x = query_23)[name = tensor<string, []>("linear_104")];
            tensor<int32, [4]> var_2180 = const()[name = tensor<string, []>("op_2180"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_23 = reshape(shape = var_2180, x = var_2179)[name = tensor<string, []>("v_23")];
            tensor<int32, [4]> value_25_perm_0 = const()[name = tensor<string, []>("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_2192 = add(x = q_67, y = encoder_layers_11_self_attn_pos_bias_u)[name = tensor<string, []>("op_2192")];
            tensor<fp32, [1, 188, 8, 128]> var_2194 = add(x = q_67, y = encoder_layers_11_self_attn_pos_bias_v)[name = tensor<string, []>("op_2194")];
            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_2196_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2196_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592127424))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592511936))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592511488)))];
            tensor<bool, []> x_251_transpose_x_0 = const()[name = tensor<string, []>("x_251_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_251_transpose_y_0 = const()[name = tensor<string, []>("x_251_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_23 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2194)[name = tensor<string, []>("transpose_234")];
            tensor<fp32, [1, 8, 188, 375]> x_251 = matmul(transpose_x = x_251_transpose_x_0, transpose_y = x_251_transpose_y_0, x = q_with_bias_v_23, y = op_2196_quantized)[name = tensor<string, []>("x_251")];
            tensor<fp32, []> const_124 = const()[name = tensor<string, []>("const_124"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_253_pad_0 = const()[name = tensor<string, []>("x_253_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_253_mode_0 = const()[name = tensor<string, []>("x_253_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_253 = pad(constant_val = const_124, mode = x_253_mode_0, pad = x_253_pad_0, x = x_251)[name = tensor<string, []>("x_253")];
            tensor<int32, [4]> var_2204 = const()[name = tensor<string, []>("op_2204"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_255 = reshape(shape = var_2204, x = x_253)[name = tensor<string, []>("x_255")];
            tensor<int32, [4]> var_2208_begin_0 = const()[name = tensor<string, []>("op_2208_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2208_end_0 = const()[name = tensor<string, []>("op_2208_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2208_end_mask_0 = const()[name = tensor<string, []>("op_2208_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_2208 = slice_by_index(begin = var_2208_begin_0, end = var_2208_end_0, end_mask = var_2208_end_mask_0, x = x_255)[name = tensor<string, []>("op_2208")];
            tensor<int32, [4]> var_2209 = const()[name = tensor<string, []>("op_2209"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_45 = reshape(shape = var_2209, x = var_2208)[name = tensor<string, []>("matrix_bd_45")];
            tensor<bool, []> matrix_ac_23_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_23_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_23_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_23_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_118_perm_0 = const()[name = tensor<string, []>("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_119_perm_0 = const()[name = tensor<string, []>("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45)[name = tensor<string, []>("transpose_232")];
            tensor<fp32, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2192)[name = tensor<string, []>("transpose_233")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_23 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = tensor<string, []>("matrix_ac_23")];
            tensor<int32, [4]> matrix_bd_47_begin_0 = const()[name = tensor<string, []>("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = tensor<string, []>("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_47 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45)[name = tensor<string, []>("matrix_bd_47")];
            tensor<fp32, [1, 8, 188, 188]> var_2218 = add(x = matrix_ac_23, y = matrix_bd_47)[name = tensor<string, []>("op_2218")];
            tensor<fp32, []> _inversed_scores_45_y_0 = const()[name = tensor<string, []>("_inversed_scores_45_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_45 = mul(x = var_2218, y = _inversed_scores_45_y_0)[name = tensor<string, []>("_inversed_scores_45")];
            tensor<fp32, [1, 8, 188, 188]> scores_47 = select(a = var_14, b = _inversed_scores_45, cond = mask_3)[name = tensor<string, []>("scores_47")];
            tensor<fp32, [1, 8, 188, 188]> var_2224 = softmax(axis = var_32, x = scores_47)[name = tensor<string, []>("op_2224")];
            tensor<fp32, [1, 8, 188, 188]> input_605 = select(a = var_13, b = var_2224, cond = mask_3)[name = tensor<string, []>("input_605")];
            tensor<bool, []> x_257_transpose_x_0 = const()[name = tensor<string, []>("x_257_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_257_transpose_y_0 = const()[name = tensor<string, []>("x_257_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_25 = transpose(perm = value_25_perm_0, x = v_23)[name = tensor<string, []>("transpose_231")];
            tensor<fp32, [1, 8, 188, 128]> x_257 = matmul(transpose_x = x_257_transpose_x_0, transpose_y = x_257_transpose_y_0, x = input_605, y = value_25)[name = tensor<string, []>("x_257")];
            tensor<int32, [4]> var_2228_perm_0 = const()[name = tensor<string, []>("op_2228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2229 = const()[name = tensor<string, []>("op_2229"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_2228 = transpose(perm = var_2228_perm_0, x = x_257)[name = tensor<string, []>("transpose_230")];
            tensor<fp32, [1, 188, 1024]> input_607 = reshape(shape = var_2229, x = var_2228)[name = tensor<string, []>("input_607")];
            tensor<fp32, [1, 188, 1024]> input_609 = linear(bias = encoder_layers_11_self_attn_linear_out_bias, weight = encoder_layers_11_self_attn_linear_out_weight_quantized, x = input_607)[name = tensor<string, []>("linear_106")];
            tensor<fp32, [1, 188, 1024]> input_611 = add(x = input_603, y = input_609)[name = tensor<string, []>("input_611")];
            tensor<int32, [1]> x_261_axes_0 = const()[name = tensor<string, []>("x_261_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_261 = layer_norm(axes = x_261_axes_0, beta = encoder_layers_11_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_11_norm_conv_weight, x = input_611)[name = tensor<string, []>("x_261")];
            tensor<int32, [3]> input_613_perm_0 = const()[name = tensor<string, []>("input_613_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_615_pad_type_0 = const()[name = tensor<string, []>("input_615_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_615_strides_0 = const()[name = tensor<string, []>("input_615_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_615_pad_0 = const()[name = tensor<string, []>("input_615_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_615_dilations_0 = const()[name = tensor<string, []>("input_615_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_615_groups_0 = const()[name = tensor<string, []>("input_615_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_613 = transpose(perm = input_613_perm_0, x = x_261)[name = tensor<string, []>("transpose_229")];
            tensor<fp32, [1, 2048, 188]> input_615 = conv(bias = encoder_layers_11_conv_pointwise_conv1_bias, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = encoder_layers_11_conv_pointwise_conv1_weight_quantized, x = input_613)[name = tensor<string, []>("input_615")];
            tensor<int32, []> x_263_split_num_splits_0 = const()[name = tensor<string, []>("x_263_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_263_split_axis_0 = const()[name = tensor<string, []>("x_263_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_263_split_0, tensor<fp32, [1, 1024, 188]> x_263_split_1 = split(axis = x_263_split_axis_0, num_splits = x_263_split_num_splits_0, x = input_615)[name = tensor<string, []>("x_263_split")];
            tensor<fp32, [1, 1024, 188]> x_263_split_1_sigmoid = sigmoid(x = x_263_split_1)[name = tensor<string, []>("x_263_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_263 = mul(x = x_263_split_0, y = x_263_split_1_sigmoid)[name = tensor<string, []>("x_263")];
            tensor<fp32, [1, 1024, 188]> input_617 = select(a = var_13, b = x_263, cond = var_339)[name = tensor<string, []>("input_617")];
            tensor<fp32, []> const_127 = const()[name = tensor<string, []>("const_127"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_619_pad_0 = const()[name = tensor<string, []>("input_619_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_619_mode_0 = const()[name = tensor<string, []>("input_619_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_619 = pad(constant_val = const_127, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617)[name = tensor<string, []>("input_619")];
            tensor<string, []> input_621_pad_type_0 = const()[name = tensor<string, []>("input_621_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_621_groups_0 = const()[name = tensor<string, []>("input_621_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_621_strides_0 = const()[name = tensor<string, []>("input_621_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_621_pad_0 = const()[name = tensor<string, []>("input_621_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_621_dilations_0 = const()[name = tensor<string, []>("input_621_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_270_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_270_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592513536))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592523904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592522816)))];
            tensor<fp32, [1024]> const_271 = const()[name = tensor<string, []>("const_271"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592528064)))];
            tensor<fp32, [1, 1024, 188]> input_623 = conv(bias = const_271, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_quantized, x = input_619)[name = tensor<string, []>("input_623")];
            tensor<fp32, [1, 1024, 188]> input_625 = silu(x = input_623)[name = tensor<string, []>("input_625")];
            tensor<string, []> x_265_pad_type_0 = const()[name = tensor<string, []>("x_265_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_265_strides_0 = const()[name = tensor<string, []>("x_265_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_265_pad_0 = const()[name = tensor<string, []>("x_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_265_dilations_0 = const()[name = tensor<string, []>("x_265_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_265_groups_0 = const()[name = tensor<string, []>("x_265_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_265 = conv(bias = encoder_layers_11_conv_pointwise_conv2_bias, dilations = x_265_dilations_0, groups = x_265_groups_0, pad = x_265_pad_0, pad_type = x_265_pad_type_0, strides = x_265_strides_0, weight = encoder_layers_11_conv_pointwise_conv2_weight_quantized, x = input_625)[name = tensor<string, []>("x_265")];
            tensor<int32, [3]> input_627_perm_0 = const()[name = tensor<string, []>("input_627_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_627 = transpose(perm = input_627_perm_0, x = x_265)[name = tensor<string, []>("transpose_228")];
            tensor<fp32, [1, 188, 1024]> input_629 = add(x = input_611, y = input_627)[name = tensor<string, []>("input_629")];
            tensor<int32, [1]> input_631_axes_0 = const()[name = tensor<string, []>("input_631_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_631 = layer_norm(axes = input_631_axes_0, beta = encoder_layers_11_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_11_norm_feed_forward2_weight, x = input_629)[name = tensor<string, []>("input_631")];
            tensor<fp32, [1, 188, 4096]> input_633 = linear(bias = encoder_layers_11_feed_forward2_linear1_bias, weight = encoder_layers_11_feed_forward2_linear1_weight_quantized, x = input_631)[name = tensor<string, []>("linear_107")];
            tensor<fp32, [1, 188, 4096]> input_635 = silu(x = input_633)[name = tensor<string, []>("input_635")];
            tensor<fp32, [1, 188, 1024]> input_639 = linear(bias = encoder_layers_11_feed_forward2_linear2_bias, weight = encoder_layers_11_feed_forward2_linear2_weight_quantized, x = input_635)[name = tensor<string, []>("linear_108")];
            tensor<fp32, []> var_2295 = const()[name = tensor<string, []>("op_2295"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2296 = mul(x = input_639, y = var_2295)[name = tensor<string, []>("op_2296")];
            tensor<fp32, [1, 188, 1024]> input_641 = add(x = input_629, y = var_2296)[name = tensor<string, []>("input_641")];
            tensor<int32, [1]> input_643_axes_0 = const()[name = tensor<string, []>("input_643_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_643 = layer_norm(axes = input_643_axes_0, beta = encoder_layers_11_norm_out_bias, epsilon = var_11, gamma = encoder_layers_11_norm_out_weight, x = input_641)[name = tensor<string, []>("input_643")];
            tensor<int32, [1]> input_645_axes_0 = const()[name = tensor<string, []>("input_645_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_645 = layer_norm(axes = input_645_axes_0, beta = encoder_layers_12_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_12_norm_feed_forward1_weight, x = input_643)[name = tensor<string, []>("input_645")];
            tensor<fp32, [1, 188, 4096]> input_647 = linear(bias = encoder_layers_12_feed_forward1_linear1_bias, weight = encoder_layers_12_feed_forward1_linear1_weight_quantized, x = input_645)[name = tensor<string, []>("linear_109")];
            tensor<fp32, [1, 188, 4096]> input_649 = silu(x = input_647)[name = tensor<string, []>("input_649")];
            tensor<fp32, [1, 188, 1024]> input_653 = linear(bias = encoder_layers_12_feed_forward1_linear2_bias, weight = encoder_layers_12_feed_forward1_linear2_weight_quantized, x = input_649)[name = tensor<string, []>("linear_110")];
            tensor<fp32, []> var_2326 = const()[name = tensor<string, []>("op_2326"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2327 = mul(x = input_653, y = var_2326)[name = tensor<string, []>("op_2327")];
            tensor<fp32, [1, 188, 1024]> input_655 = add(x = input_643, y = var_2327)[name = tensor<string, []>("input_655")];
            tensor<int32, [1]> query_25_axes_0 = const()[name = tensor<string, []>("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_25 = layer_norm(axes = query_25_axes_0, beta = encoder_layers_12_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_12_norm_self_att_weight, x = input_655)[name = tensor<string, []>("query_25")];
            tensor<fp32, [1, 188, 1024]> var_2343 = linear(bias = encoder_layers_12_self_attn_linear_q_bias, weight = encoder_layers_12_self_attn_linear_q_weight_quantized, x = query_25)[name = tensor<string, []>("linear_111")];
            tensor<int32, [4]> var_2344 = const()[name = tensor<string, []>("op_2344"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_73 = reshape(shape = var_2344, x = var_2343)[name = tensor<string, []>("q_73")];
            tensor<fp32, [1, 188, 1024]> var_2348 = linear(bias = encoder_layers_12_self_attn_linear_k_bias, weight = encoder_layers_12_self_attn_linear_k_weight_quantized, x = query_25)[name = tensor<string, []>("linear_112")];
            tensor<int32, [4]> var_2349 = const()[name = tensor<string, []>("op_2349"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_49 = reshape(shape = var_2349, x = var_2348)[name = tensor<string, []>("k_49")];
            tensor<fp32, [1, 188, 1024]> var_2353 = linear(bias = encoder_layers_12_self_attn_linear_v_bias, weight = encoder_layers_12_self_attn_linear_v_weight_quantized, x = query_25)[name = tensor<string, []>("linear_113")];
            tensor<int32, [4]> var_2354 = const()[name = tensor<string, []>("op_2354"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_25 = reshape(shape = var_2354, x = var_2353)[name = tensor<string, []>("v_25")];
            tensor<int32, [4]> value_27_perm_0 = const()[name = tensor<string, []>("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_2366 = add(x = q_73, y = encoder_layers_12_self_attn_pos_bias_u)[name = tensor<string, []>("op_2366")];
            tensor<fp32, [1, 188, 8, 128]> var_2368 = add(x = q_73, y = encoder_layers_12_self_attn_pos_bias_v)[name = tensor<string, []>("op_2368")];
            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_2370_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2370_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592532224))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592916736))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592916288)))];
            tensor<bool, []> x_273_transpose_x_0 = const()[name = tensor<string, []>("x_273_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_273_transpose_y_0 = const()[name = tensor<string, []>("x_273_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_25 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2368)[name = tensor<string, []>("transpose_227")];
            tensor<fp32, [1, 8, 188, 375]> x_273 = matmul(transpose_x = x_273_transpose_x_0, transpose_y = x_273_transpose_y_0, x = q_with_bias_v_25, y = op_2370_quantized)[name = tensor<string, []>("x_273")];
            tensor<fp32, []> const_134 = const()[name = tensor<string, []>("const_134"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_275_pad_0 = const()[name = tensor<string, []>("x_275_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_275_mode_0 = const()[name = tensor<string, []>("x_275_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_275 = pad(constant_val = const_134, mode = x_275_mode_0, pad = x_275_pad_0, x = x_273)[name = tensor<string, []>("x_275")];
            tensor<int32, [4]> var_2378 = const()[name = tensor<string, []>("op_2378"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_277 = reshape(shape = var_2378, x = x_275)[name = tensor<string, []>("x_277")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = tensor<string, []>("op_2382_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = tensor<string, []>("op_2382_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = tensor<string, []>("op_2382_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_2382 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = x_277)[name = tensor<string, []>("op_2382")];
            tensor<int32, [4]> var_2383 = const()[name = tensor<string, []>("op_2383"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_49 = reshape(shape = var_2383, x = var_2382)[name = tensor<string, []>("matrix_bd_49")];
            tensor<bool, []> matrix_ac_25_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_25_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_120_perm_0 = const()[name = tensor<string, []>("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_121_perm_0 = const()[name = tensor<string, []>("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49)[name = tensor<string, []>("transpose_225")];
            tensor<fp32, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2366)[name = tensor<string, []>("transpose_226")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_25 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = tensor<string, []>("matrix_ac_25")];
            tensor<int32, [4]> matrix_bd_51_begin_0 = const()[name = tensor<string, []>("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = tensor<string, []>("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_51 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49)[name = tensor<string, []>("matrix_bd_51")];
            tensor<fp32, [1, 8, 188, 188]> var_2392 = add(x = matrix_ac_25, y = matrix_bd_51)[name = tensor<string, []>("op_2392")];
            tensor<fp32, []> _inversed_scores_49_y_0 = const()[name = tensor<string, []>("_inversed_scores_49_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_49 = mul(x = var_2392, y = _inversed_scores_49_y_0)[name = tensor<string, []>("_inversed_scores_49")];
            tensor<fp32, [1, 8, 188, 188]> scores_51 = select(a = var_14, b = _inversed_scores_49, cond = mask_3)[name = tensor<string, []>("scores_51")];
            tensor<fp32, [1, 8, 188, 188]> var_2398 = softmax(axis = var_32, x = scores_51)[name = tensor<string, []>("op_2398")];
            tensor<fp32, [1, 8, 188, 188]> input_657 = select(a = var_13, b = var_2398, cond = mask_3)[name = tensor<string, []>("input_657")];
            tensor<bool, []> x_279_transpose_x_0 = const()[name = tensor<string, []>("x_279_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_279_transpose_y_0 = const()[name = tensor<string, []>("x_279_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_27 = transpose(perm = value_27_perm_0, x = v_25)[name = tensor<string, []>("transpose_224")];
            tensor<fp32, [1, 8, 188, 128]> x_279 = matmul(transpose_x = x_279_transpose_x_0, transpose_y = x_279_transpose_y_0, x = input_657, y = value_27)[name = tensor<string, []>("x_279")];
            tensor<int32, [4]> var_2402_perm_0 = const()[name = tensor<string, []>("op_2402_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2403 = const()[name = tensor<string, []>("op_2403"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_2402 = transpose(perm = var_2402_perm_0, x = x_279)[name = tensor<string, []>("transpose_223")];
            tensor<fp32, [1, 188, 1024]> input_659 = reshape(shape = var_2403, x = var_2402)[name = tensor<string, []>("input_659")];
            tensor<fp32, [1, 188, 1024]> input_661 = linear(bias = encoder_layers_12_self_attn_linear_out_bias, weight = encoder_layers_12_self_attn_linear_out_weight_quantized, x = input_659)[name = tensor<string, []>("linear_115")];
            tensor<fp32, [1, 188, 1024]> input_663 = add(x = input_655, y = input_661)[name = tensor<string, []>("input_663")];
            tensor<int32, [1]> x_283_axes_0 = const()[name = tensor<string, []>("x_283_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_283 = layer_norm(axes = x_283_axes_0, beta = encoder_layers_12_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_12_norm_conv_weight, x = input_663)[name = tensor<string, []>("x_283")];
            tensor<int32, [3]> input_665_perm_0 = const()[name = tensor<string, []>("input_665_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_667_pad_type_0 = const()[name = tensor<string, []>("input_667_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_667_strides_0 = const()[name = tensor<string, []>("input_667_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_667_pad_0 = const()[name = tensor<string, []>("input_667_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_667_dilations_0 = const()[name = tensor<string, []>("input_667_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_667_groups_0 = const()[name = tensor<string, []>("input_667_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_665 = transpose(perm = input_665_perm_0, x = x_283)[name = tensor<string, []>("transpose_222")];
            tensor<fp32, [1, 2048, 188]> input_667 = conv(bias = encoder_layers_12_conv_pointwise_conv1_bias, dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = encoder_layers_12_conv_pointwise_conv1_weight_quantized, x = input_665)[name = tensor<string, []>("input_667")];
            tensor<int32, []> x_285_split_num_splits_0 = const()[name = tensor<string, []>("x_285_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_285_split_axis_0 = const()[name = tensor<string, []>("x_285_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_285_split_0, tensor<fp32, [1, 1024, 188]> x_285_split_1 = split(axis = x_285_split_axis_0, num_splits = x_285_split_num_splits_0, x = input_667)[name = tensor<string, []>("x_285_split")];
            tensor<fp32, [1, 1024, 188]> x_285_split_1_sigmoid = sigmoid(x = x_285_split_1)[name = tensor<string, []>("x_285_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_285 = mul(x = x_285_split_0, y = x_285_split_1_sigmoid)[name = tensor<string, []>("x_285")];
            tensor<fp32, [1, 1024, 188]> input_669 = select(a = var_13, b = x_285, cond = var_339)[name = tensor<string, []>("input_669")];
            tensor<fp32, []> const_137 = const()[name = tensor<string, []>("const_137"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_671_pad_0 = const()[name = tensor<string, []>("input_671_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_671_mode_0 = const()[name = tensor<string, []>("input_671_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_671 = pad(constant_val = const_137, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669)[name = tensor<string, []>("input_671")];
            tensor<string, []> input_673_pad_type_0 = const()[name = tensor<string, []>("input_673_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_673_groups_0 = const()[name = tensor<string, []>("input_673_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_673_strides_0 = const()[name = tensor<string, []>("input_673_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_673_pad_0 = const()[name = tensor<string, []>("input_673_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_673_dilations_0 = const()[name = tensor<string, []>("input_673_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_272_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_272_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592918336))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592928704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592927616)))];
            tensor<fp32, [1024]> const_273 = const()[name = tensor<string, []>("const_273"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592932864)))];
            tensor<fp32, [1, 1024, 188]> input_675 = conv(bias = const_273, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_quantized, x = input_671)[name = tensor<string, []>("input_675")];
            tensor<fp32, [1, 1024, 188]> input_677 = silu(x = input_675)[name = tensor<string, []>("input_677")];
            tensor<string, []> x_287_pad_type_0 = const()[name = tensor<string, []>("x_287_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_287_strides_0 = const()[name = tensor<string, []>("x_287_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_287_pad_0 = const()[name = tensor<string, []>("x_287_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_287_dilations_0 = const()[name = tensor<string, []>("x_287_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_287_groups_0 = const()[name = tensor<string, []>("x_287_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_287 = conv(bias = encoder_layers_12_conv_pointwise_conv2_bias, dilations = x_287_dilations_0, groups = x_287_groups_0, pad = x_287_pad_0, pad_type = x_287_pad_type_0, strides = x_287_strides_0, weight = encoder_layers_12_conv_pointwise_conv2_weight_quantized, x = input_677)[name = tensor<string, []>("x_287")];
            tensor<int32, [3]> input_679_perm_0 = const()[name = tensor<string, []>("input_679_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_679 = transpose(perm = input_679_perm_0, x = x_287)[name = tensor<string, []>("transpose_221")];
            tensor<fp32, [1, 188, 1024]> input_681 = add(x = input_663, y = input_679)[name = tensor<string, []>("input_681")];
            tensor<int32, [1]> input_683_axes_0 = const()[name = tensor<string, []>("input_683_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_683 = layer_norm(axes = input_683_axes_0, beta = encoder_layers_12_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_12_norm_feed_forward2_weight, x = input_681)[name = tensor<string, []>("input_683")];
            tensor<fp32, [1, 188, 4096]> input_685 = linear(bias = encoder_layers_12_feed_forward2_linear1_bias, weight = encoder_layers_12_feed_forward2_linear1_weight_quantized, x = input_683)[name = tensor<string, []>("linear_116")];
            tensor<fp32, [1, 188, 4096]> input_687 = silu(x = input_685)[name = tensor<string, []>("input_687")];
            tensor<fp32, [1, 188, 1024]> input_691 = linear(bias = encoder_layers_12_feed_forward2_linear2_bias, weight = encoder_layers_12_feed_forward2_linear2_weight_quantized, x = input_687)[name = tensor<string, []>("linear_117")];
            tensor<fp32, []> var_2469 = const()[name = tensor<string, []>("op_2469"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2470 = mul(x = input_691, y = var_2469)[name = tensor<string, []>("op_2470")];
            tensor<fp32, [1, 188, 1024]> input_693 = add(x = input_681, y = var_2470)[name = tensor<string, []>("input_693")];
            tensor<int32, [1]> input_695_axes_0 = const()[name = tensor<string, []>("input_695_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_695 = layer_norm(axes = input_695_axes_0, beta = encoder_layers_12_norm_out_bias, epsilon = var_11, gamma = encoder_layers_12_norm_out_weight, x = input_693)[name = tensor<string, []>("input_695")];
            tensor<int32, [1]> input_697_axes_0 = const()[name = tensor<string, []>("input_697_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_697 = layer_norm(axes = input_697_axes_0, beta = encoder_layers_13_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_13_norm_feed_forward1_weight, x = input_695)[name = tensor<string, []>("input_697")];
            tensor<fp32, [1, 188, 4096]> input_699 = linear(bias = encoder_layers_13_feed_forward1_linear1_bias, weight = encoder_layers_13_feed_forward1_linear1_weight_quantized, x = input_697)[name = tensor<string, []>("linear_118")];
            tensor<fp32, [1, 188, 4096]> input_701 = silu(x = input_699)[name = tensor<string, []>("input_701")];
            tensor<fp32, [1, 188, 1024]> input_705 = linear(bias = encoder_layers_13_feed_forward1_linear2_bias, weight = encoder_layers_13_feed_forward1_linear2_weight_quantized, x = input_701)[name = tensor<string, []>("linear_119")];
            tensor<fp32, []> var_2500 = const()[name = tensor<string, []>("op_2500"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2501 = mul(x = input_705, y = var_2500)[name = tensor<string, []>("op_2501")];
            tensor<fp32, [1, 188, 1024]> input_707 = add(x = input_695, y = var_2501)[name = tensor<string, []>("input_707")];
            tensor<int32, [1]> query_27_axes_0 = const()[name = tensor<string, []>("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_27 = layer_norm(axes = query_27_axes_0, beta = encoder_layers_13_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_13_norm_self_att_weight, x = input_707)[name = tensor<string, []>("query_27")];
            tensor<fp32, [1, 188, 1024]> var_2517 = linear(bias = encoder_layers_13_self_attn_linear_q_bias, weight = encoder_layers_13_self_attn_linear_q_weight_quantized, x = query_27)[name = tensor<string, []>("linear_120")];
            tensor<int32, [4]> var_2518 = const()[name = tensor<string, []>("op_2518"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_79 = reshape(shape = var_2518, x = var_2517)[name = tensor<string, []>("q_79")];
            tensor<fp32, [1, 188, 1024]> var_2522 = linear(bias = encoder_layers_13_self_attn_linear_k_bias, weight = encoder_layers_13_self_attn_linear_k_weight_quantized, x = query_27)[name = tensor<string, []>("linear_121")];
            tensor<int32, [4]> var_2523 = const()[name = tensor<string, []>("op_2523"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_53 = reshape(shape = var_2523, x = var_2522)[name = tensor<string, []>("k_53")];
            tensor<fp32, [1, 188, 1024]> var_2527 = linear(bias = encoder_layers_13_self_attn_linear_v_bias, weight = encoder_layers_13_self_attn_linear_v_weight_quantized, x = query_27)[name = tensor<string, []>("linear_122")];
            tensor<int32, [4]> var_2528 = const()[name = tensor<string, []>("op_2528"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_27 = reshape(shape = var_2528, x = var_2527)[name = tensor<string, []>("v_27")];
            tensor<int32, [4]> value_29_perm_0 = const()[name = tensor<string, []>("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_2540 = add(x = q_79, y = encoder_layers_13_self_attn_pos_bias_u)[name = tensor<string, []>("op_2540")];
            tensor<fp32, [1, 188, 8, 128]> var_2542 = add(x = q_79, y = encoder_layers_13_self_attn_pos_bias_v)[name = tensor<string, []>("op_2542")];
            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_2544_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2544_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592937024))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593321536))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593321088)))];
            tensor<bool, []> x_295_transpose_x_0 = const()[name = tensor<string, []>("x_295_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_295_transpose_y_0 = const()[name = tensor<string, []>("x_295_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_27 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2542)[name = tensor<string, []>("transpose_220")];
            tensor<fp32, [1, 8, 188, 375]> x_295 = matmul(transpose_x = x_295_transpose_x_0, transpose_y = x_295_transpose_y_0, x = q_with_bias_v_27, y = op_2544_quantized)[name = tensor<string, []>("x_295")];
            tensor<fp32, []> const_144 = const()[name = tensor<string, []>("const_144"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_297_pad_0 = const()[name = tensor<string, []>("x_297_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_297_mode_0 = const()[name = tensor<string, []>("x_297_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_297 = pad(constant_val = const_144, mode = x_297_mode_0, pad = x_297_pad_0, x = x_295)[name = tensor<string, []>("x_297")];
            tensor<int32, [4]> var_2552 = const()[name = tensor<string, []>("op_2552"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_299 = reshape(shape = var_2552, x = x_297)[name = tensor<string, []>("x_299")];
            tensor<int32, [4]> var_2556_begin_0 = const()[name = tensor<string, []>("op_2556_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2556_end_0 = const()[name = tensor<string, []>("op_2556_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2556_end_mask_0 = const()[name = tensor<string, []>("op_2556_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_2556 = slice_by_index(begin = var_2556_begin_0, end = var_2556_end_0, end_mask = var_2556_end_mask_0, x = x_299)[name = tensor<string, []>("op_2556")];
            tensor<int32, [4]> var_2557 = const()[name = tensor<string, []>("op_2557"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_53 = reshape(shape = var_2557, x = var_2556)[name = tensor<string, []>("matrix_bd_53")];
            tensor<bool, []> matrix_ac_27_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_27_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_27_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_27_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_122_perm_0 = const()[name = tensor<string, []>("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_123_perm_0 = const()[name = tensor<string, []>("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53)[name = tensor<string, []>("transpose_218")];
            tensor<fp32, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2540)[name = tensor<string, []>("transpose_219")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_27 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = tensor<string, []>("matrix_ac_27")];
            tensor<int32, [4]> matrix_bd_55_begin_0 = const()[name = tensor<string, []>("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = tensor<string, []>("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_55 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53)[name = tensor<string, []>("matrix_bd_55")];
            tensor<fp32, [1, 8, 188, 188]> var_2566 = add(x = matrix_ac_27, y = matrix_bd_55)[name = tensor<string, []>("op_2566")];
            tensor<fp32, []> _inversed_scores_53_y_0 = const()[name = tensor<string, []>("_inversed_scores_53_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_53 = mul(x = var_2566, y = _inversed_scores_53_y_0)[name = tensor<string, []>("_inversed_scores_53")];
            tensor<fp32, [1, 8, 188, 188]> scores_55 = select(a = var_14, b = _inversed_scores_53, cond = mask_3)[name = tensor<string, []>("scores_55")];
            tensor<fp32, [1, 8, 188, 188]> var_2572 = softmax(axis = var_32, x = scores_55)[name = tensor<string, []>("op_2572")];
            tensor<fp32, [1, 8, 188, 188]> input_709 = select(a = var_13, b = var_2572, cond = mask_3)[name = tensor<string, []>("input_709")];
            tensor<bool, []> x_301_transpose_x_0 = const()[name = tensor<string, []>("x_301_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_301_transpose_y_0 = const()[name = tensor<string, []>("x_301_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_29 = transpose(perm = value_29_perm_0, x = v_27)[name = tensor<string, []>("transpose_217")];
            tensor<fp32, [1, 8, 188, 128]> x_301 = matmul(transpose_x = x_301_transpose_x_0, transpose_y = x_301_transpose_y_0, x = input_709, y = value_29)[name = tensor<string, []>("x_301")];
            tensor<int32, [4]> var_2576_perm_0 = const()[name = tensor<string, []>("op_2576_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2577 = const()[name = tensor<string, []>("op_2577"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_2576 = transpose(perm = var_2576_perm_0, x = x_301)[name = tensor<string, []>("transpose_216")];
            tensor<fp32, [1, 188, 1024]> input_711 = reshape(shape = var_2577, x = var_2576)[name = tensor<string, []>("input_711")];
            tensor<fp32, [1, 188, 1024]> input_713 = linear(bias = encoder_layers_13_self_attn_linear_out_bias, weight = encoder_layers_13_self_attn_linear_out_weight_quantized, x = input_711)[name = tensor<string, []>("linear_124")];
            tensor<fp32, [1, 188, 1024]> input_715 = add(x = input_707, y = input_713)[name = tensor<string, []>("input_715")];
            tensor<int32, [1]> x_305_axes_0 = const()[name = tensor<string, []>("x_305_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_305 = layer_norm(axes = x_305_axes_0, beta = encoder_layers_13_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_13_norm_conv_weight, x = input_715)[name = tensor<string, []>("x_305")];
            tensor<int32, [3]> input_717_perm_0 = const()[name = tensor<string, []>("input_717_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_719_pad_type_0 = const()[name = tensor<string, []>("input_719_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_719_strides_0 = const()[name = tensor<string, []>("input_719_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_719_pad_0 = const()[name = tensor<string, []>("input_719_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_719_dilations_0 = const()[name = tensor<string, []>("input_719_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_719_groups_0 = const()[name = tensor<string, []>("input_719_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_717 = transpose(perm = input_717_perm_0, x = x_305)[name = tensor<string, []>("transpose_215")];
            tensor<fp32, [1, 2048, 188]> input_719 = conv(bias = encoder_layers_13_conv_pointwise_conv1_bias, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = encoder_layers_13_conv_pointwise_conv1_weight_quantized, x = input_717)[name = tensor<string, []>("input_719")];
            tensor<int32, []> x_307_split_num_splits_0 = const()[name = tensor<string, []>("x_307_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_307_split_axis_0 = const()[name = tensor<string, []>("x_307_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_307_split_0, tensor<fp32, [1, 1024, 188]> x_307_split_1 = split(axis = x_307_split_axis_0, num_splits = x_307_split_num_splits_0, x = input_719)[name = tensor<string, []>("x_307_split")];
            tensor<fp32, [1, 1024, 188]> x_307_split_1_sigmoid = sigmoid(x = x_307_split_1)[name = tensor<string, []>("x_307_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_307 = mul(x = x_307_split_0, y = x_307_split_1_sigmoid)[name = tensor<string, []>("x_307")];
            tensor<fp32, [1, 1024, 188]> input_721 = select(a = var_13, b = x_307, cond = var_339)[name = tensor<string, []>("input_721")];
            tensor<fp32, []> const_147 = const()[name = tensor<string, []>("const_147"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_723_pad_0 = const()[name = tensor<string, []>("input_723_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_723_mode_0 = const()[name = tensor<string, []>("input_723_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_723 = pad(constant_val = const_147, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721)[name = tensor<string, []>("input_723")];
            tensor<string, []> input_725_pad_type_0 = const()[name = tensor<string, []>("input_725_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_725_groups_0 = const()[name = tensor<string, []>("input_725_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_725_strides_0 = const()[name = tensor<string, []>("input_725_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_725_pad_0 = const()[name = tensor<string, []>("input_725_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_725_dilations_0 = const()[name = tensor<string, []>("input_725_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_274_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_274_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593323136))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593333504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593332416)))];
            tensor<fp32, [1024]> const_275 = const()[name = tensor<string, []>("const_275"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593337664)))];
            tensor<fp32, [1, 1024, 188]> input_727 = conv(bias = const_275, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_quantized, x = input_723)[name = tensor<string, []>("input_727")];
            tensor<fp32, [1, 1024, 188]> input_729 = silu(x = input_727)[name = tensor<string, []>("input_729")];
            tensor<string, []> x_309_pad_type_0 = const()[name = tensor<string, []>("x_309_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_309_strides_0 = const()[name = tensor<string, []>("x_309_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_309_pad_0 = const()[name = tensor<string, []>("x_309_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_309_dilations_0 = const()[name = tensor<string, []>("x_309_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_309_groups_0 = const()[name = tensor<string, []>("x_309_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_309 = conv(bias = encoder_layers_13_conv_pointwise_conv2_bias, dilations = x_309_dilations_0, groups = x_309_groups_0, pad = x_309_pad_0, pad_type = x_309_pad_type_0, strides = x_309_strides_0, weight = encoder_layers_13_conv_pointwise_conv2_weight_quantized, x = input_729)[name = tensor<string, []>("x_309")];
            tensor<int32, [3]> input_731_perm_0 = const()[name = tensor<string, []>("input_731_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_731 = transpose(perm = input_731_perm_0, x = x_309)[name = tensor<string, []>("transpose_214")];
            tensor<fp32, [1, 188, 1024]> input_733 = add(x = input_715, y = input_731)[name = tensor<string, []>("input_733")];
            tensor<int32, [1]> input_735_axes_0 = const()[name = tensor<string, []>("input_735_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_735 = layer_norm(axes = input_735_axes_0, beta = encoder_layers_13_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_13_norm_feed_forward2_weight, x = input_733)[name = tensor<string, []>("input_735")];
            tensor<fp32, [1, 188, 4096]> input_737 = linear(bias = encoder_layers_13_feed_forward2_linear1_bias, weight = encoder_layers_13_feed_forward2_linear1_weight_quantized, x = input_735)[name = tensor<string, []>("linear_125")];
            tensor<fp32, [1, 188, 4096]> input_739 = silu(x = input_737)[name = tensor<string, []>("input_739")];
            tensor<fp32, [1, 188, 1024]> input_743 = linear(bias = encoder_layers_13_feed_forward2_linear2_bias, weight = encoder_layers_13_feed_forward2_linear2_weight_quantized, x = input_739)[name = tensor<string, []>("linear_126")];
            tensor<fp32, []> var_2643 = const()[name = tensor<string, []>("op_2643"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2644 = mul(x = input_743, y = var_2643)[name = tensor<string, []>("op_2644")];
            tensor<fp32, [1, 188, 1024]> input_745 = add(x = input_733, y = var_2644)[name = tensor<string, []>("input_745")];
            tensor<int32, [1]> input_747_axes_0 = const()[name = tensor<string, []>("input_747_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_747 = layer_norm(axes = input_747_axes_0, beta = encoder_layers_13_norm_out_bias, epsilon = var_11, gamma = encoder_layers_13_norm_out_weight, x = input_745)[name = tensor<string, []>("input_747")];
            tensor<int32, [1]> input_749_axes_0 = const()[name = tensor<string, []>("input_749_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_749 = layer_norm(axes = input_749_axes_0, beta = encoder_layers_14_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_14_norm_feed_forward1_weight, x = input_747)[name = tensor<string, []>("input_749")];
            tensor<fp32, [1, 188, 4096]> input_751 = linear(bias = encoder_layers_14_feed_forward1_linear1_bias, weight = encoder_layers_14_feed_forward1_linear1_weight_quantized, x = input_749)[name = tensor<string, []>("linear_127")];
            tensor<fp32, [1, 188, 4096]> input_753 = silu(x = input_751)[name = tensor<string, []>("input_753")];
            tensor<fp32, [1, 188, 1024]> input_757 = linear(bias = encoder_layers_14_feed_forward1_linear2_bias, weight = encoder_layers_14_feed_forward1_linear2_weight_quantized, x = input_753)[name = tensor<string, []>("linear_128")];
            tensor<fp32, []> var_2674 = const()[name = tensor<string, []>("op_2674"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2675 = mul(x = input_757, y = var_2674)[name = tensor<string, []>("op_2675")];
            tensor<fp32, [1, 188, 1024]> input_759 = add(x = input_747, y = var_2675)[name = tensor<string, []>("input_759")];
            tensor<int32, [1]> query_29_axes_0 = const()[name = tensor<string, []>("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_29 = layer_norm(axes = query_29_axes_0, beta = encoder_layers_14_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_14_norm_self_att_weight, x = input_759)[name = tensor<string, []>("query_29")];
            tensor<fp32, [1, 188, 1024]> var_2691 = linear(bias = encoder_layers_14_self_attn_linear_q_bias, weight = encoder_layers_14_self_attn_linear_q_weight_quantized, x = query_29)[name = tensor<string, []>("linear_129")];
            tensor<int32, [4]> var_2692 = const()[name = tensor<string, []>("op_2692"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_85 = reshape(shape = var_2692, x = var_2691)[name = tensor<string, []>("q_85")];
            tensor<fp32, [1, 188, 1024]> var_2696 = linear(bias = encoder_layers_14_self_attn_linear_k_bias, weight = encoder_layers_14_self_attn_linear_k_weight_quantized, x = query_29)[name = tensor<string, []>("linear_130")];
            tensor<int32, [4]> var_2697 = const()[name = tensor<string, []>("op_2697"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_57 = reshape(shape = var_2697, x = var_2696)[name = tensor<string, []>("k_57")];
            tensor<fp32, [1, 188, 1024]> var_2701 = linear(bias = encoder_layers_14_self_attn_linear_v_bias, weight = encoder_layers_14_self_attn_linear_v_weight_quantized, x = query_29)[name = tensor<string, []>("linear_131")];
            tensor<int32, [4]> var_2702 = const()[name = tensor<string, []>("op_2702"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_29 = reshape(shape = var_2702, x = var_2701)[name = tensor<string, []>("v_29")];
            tensor<int32, [4]> value_31_perm_0 = const()[name = tensor<string, []>("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_2714 = add(x = q_85, y = encoder_layers_14_self_attn_pos_bias_u)[name = tensor<string, []>("op_2714")];
            tensor<fp32, [1, 188, 8, 128]> var_2716 = add(x = q_85, y = encoder_layers_14_self_attn_pos_bias_v)[name = tensor<string, []>("op_2716")];
            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_2718_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2718_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593341824))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593726336))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593725888)))];
            tensor<bool, []> x_317_transpose_x_0 = const()[name = tensor<string, []>("x_317_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_317_transpose_y_0 = const()[name = tensor<string, []>("x_317_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_29 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2716)[name = tensor<string, []>("transpose_213")];
            tensor<fp32, [1, 8, 188, 375]> x_317 = matmul(transpose_x = x_317_transpose_x_0, transpose_y = x_317_transpose_y_0, x = q_with_bias_v_29, y = op_2718_quantized)[name = tensor<string, []>("x_317")];
            tensor<fp32, []> const_154 = const()[name = tensor<string, []>("const_154"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_319_pad_0 = const()[name = tensor<string, []>("x_319_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_319_mode_0 = const()[name = tensor<string, []>("x_319_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_319 = pad(constant_val = const_154, mode = x_319_mode_0, pad = x_319_pad_0, x = x_317)[name = tensor<string, []>("x_319")];
            tensor<int32, [4]> var_2726 = const()[name = tensor<string, []>("op_2726"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_321 = reshape(shape = var_2726, x = x_319)[name = tensor<string, []>("x_321")];
            tensor<int32, [4]> var_2730_begin_0 = const()[name = tensor<string, []>("op_2730_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2730_end_0 = const()[name = tensor<string, []>("op_2730_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2730_end_mask_0 = const()[name = tensor<string, []>("op_2730_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_2730 = slice_by_index(begin = var_2730_begin_0, end = var_2730_end_0, end_mask = var_2730_end_mask_0, x = x_321)[name = tensor<string, []>("op_2730")];
            tensor<int32, [4]> var_2731 = const()[name = tensor<string, []>("op_2731"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_57 = reshape(shape = var_2731, x = var_2730)[name = tensor<string, []>("matrix_bd_57")];
            tensor<bool, []> matrix_ac_29_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_29_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_29_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_29_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_124_perm_0 = const()[name = tensor<string, []>("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_125_perm_0 = const()[name = tensor<string, []>("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57)[name = tensor<string, []>("transpose_211")];
            tensor<fp32, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2714)[name = tensor<string, []>("transpose_212")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_29 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = tensor<string, []>("matrix_ac_29")];
            tensor<int32, [4]> matrix_bd_59_begin_0 = const()[name = tensor<string, []>("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = tensor<string, []>("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_59 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57)[name = tensor<string, []>("matrix_bd_59")];
            tensor<fp32, [1, 8, 188, 188]> var_2740 = add(x = matrix_ac_29, y = matrix_bd_59)[name = tensor<string, []>("op_2740")];
            tensor<fp32, []> _inversed_scores_57_y_0 = const()[name = tensor<string, []>("_inversed_scores_57_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_57 = mul(x = var_2740, y = _inversed_scores_57_y_0)[name = tensor<string, []>("_inversed_scores_57")];
            tensor<fp32, [1, 8, 188, 188]> scores_59 = select(a = var_14, b = _inversed_scores_57, cond = mask_3)[name = tensor<string, []>("scores_59")];
            tensor<fp32, [1, 8, 188, 188]> var_2746 = softmax(axis = var_32, x = scores_59)[name = tensor<string, []>("op_2746")];
            tensor<fp32, [1, 8, 188, 188]> input_761 = select(a = var_13, b = var_2746, cond = mask_3)[name = tensor<string, []>("input_761")];
            tensor<bool, []> x_323_transpose_x_0 = const()[name = tensor<string, []>("x_323_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_323_transpose_y_0 = const()[name = tensor<string, []>("x_323_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_31 = transpose(perm = value_31_perm_0, x = v_29)[name = tensor<string, []>("transpose_210")];
            tensor<fp32, [1, 8, 188, 128]> x_323 = matmul(transpose_x = x_323_transpose_x_0, transpose_y = x_323_transpose_y_0, x = input_761, y = value_31)[name = tensor<string, []>("x_323")];
            tensor<int32, [4]> var_2750_perm_0 = const()[name = tensor<string, []>("op_2750_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2751 = const()[name = tensor<string, []>("op_2751"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_2750 = transpose(perm = var_2750_perm_0, x = x_323)[name = tensor<string, []>("transpose_209")];
            tensor<fp32, [1, 188, 1024]> input_763 = reshape(shape = var_2751, x = var_2750)[name = tensor<string, []>("input_763")];
            tensor<fp32, [1, 188, 1024]> input_765 = linear(bias = encoder_layers_14_self_attn_linear_out_bias, weight = encoder_layers_14_self_attn_linear_out_weight_quantized, x = input_763)[name = tensor<string, []>("linear_133")];
            tensor<fp32, [1, 188, 1024]> input_767 = add(x = input_759, y = input_765)[name = tensor<string, []>("input_767")];
            tensor<int32, [1]> x_327_axes_0 = const()[name = tensor<string, []>("x_327_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_327 = layer_norm(axes = x_327_axes_0, beta = encoder_layers_14_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_14_norm_conv_weight, x = input_767)[name = tensor<string, []>("x_327")];
            tensor<int32, [3]> input_769_perm_0 = const()[name = tensor<string, []>("input_769_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_771_pad_type_0 = const()[name = tensor<string, []>("input_771_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_771_strides_0 = const()[name = tensor<string, []>("input_771_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_771_pad_0 = const()[name = tensor<string, []>("input_771_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_771_dilations_0 = const()[name = tensor<string, []>("input_771_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_771_groups_0 = const()[name = tensor<string, []>("input_771_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_769 = transpose(perm = input_769_perm_0, x = x_327)[name = tensor<string, []>("transpose_208")];
            tensor<fp32, [1, 2048, 188]> input_771 = conv(bias = encoder_layers_14_conv_pointwise_conv1_bias, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = encoder_layers_14_conv_pointwise_conv1_weight_quantized, x = input_769)[name = tensor<string, []>("input_771")];
            tensor<int32, []> x_329_split_num_splits_0 = const()[name = tensor<string, []>("x_329_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_329_split_axis_0 = const()[name = tensor<string, []>("x_329_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_329_split_0, tensor<fp32, [1, 1024, 188]> x_329_split_1 = split(axis = x_329_split_axis_0, num_splits = x_329_split_num_splits_0, x = input_771)[name = tensor<string, []>("x_329_split")];
            tensor<fp32, [1, 1024, 188]> x_329_split_1_sigmoid = sigmoid(x = x_329_split_1)[name = tensor<string, []>("x_329_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_329 = mul(x = x_329_split_0, y = x_329_split_1_sigmoid)[name = tensor<string, []>("x_329")];
            tensor<fp32, [1, 1024, 188]> input_773 = select(a = var_13, b = x_329, cond = var_339)[name = tensor<string, []>("input_773")];
            tensor<fp32, []> const_157 = const()[name = tensor<string, []>("const_157"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_775_pad_0 = const()[name = tensor<string, []>("input_775_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_775_mode_0 = const()[name = tensor<string, []>("input_775_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_775 = pad(constant_val = const_157, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773)[name = tensor<string, []>("input_775")];
            tensor<string, []> input_777_pad_type_0 = const()[name = tensor<string, []>("input_777_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_777_groups_0 = const()[name = tensor<string, []>("input_777_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_777_strides_0 = const()[name = tensor<string, []>("input_777_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_777_pad_0 = const()[name = tensor<string, []>("input_777_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_777_dilations_0 = const()[name = tensor<string, []>("input_777_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_276_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_276_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593727936))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593738304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593737216)))];
            tensor<fp32, [1024]> const_277 = const()[name = tensor<string, []>("const_277"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593742464)))];
            tensor<fp32, [1, 1024, 188]> input_779 = conv(bias = const_277, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_quantized, x = input_775)[name = tensor<string, []>("input_779")];
            tensor<fp32, [1, 1024, 188]> input_781 = silu(x = input_779)[name = tensor<string, []>("input_781")];
            tensor<string, []> x_331_pad_type_0 = const()[name = tensor<string, []>("x_331_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_331_strides_0 = const()[name = tensor<string, []>("x_331_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_331_pad_0 = const()[name = tensor<string, []>("x_331_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_331_dilations_0 = const()[name = tensor<string, []>("x_331_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_331_groups_0 = const()[name = tensor<string, []>("x_331_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_331 = conv(bias = encoder_layers_14_conv_pointwise_conv2_bias, dilations = x_331_dilations_0, groups = x_331_groups_0, pad = x_331_pad_0, pad_type = x_331_pad_type_0, strides = x_331_strides_0, weight = encoder_layers_14_conv_pointwise_conv2_weight_quantized, x = input_781)[name = tensor<string, []>("x_331")];
            tensor<int32, [3]> input_783_perm_0 = const()[name = tensor<string, []>("input_783_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_783 = transpose(perm = input_783_perm_0, x = x_331)[name = tensor<string, []>("transpose_207")];
            tensor<fp32, [1, 188, 1024]> input_785 = add(x = input_767, y = input_783)[name = tensor<string, []>("input_785")];
            tensor<int32, [1]> input_787_axes_0 = const()[name = tensor<string, []>("input_787_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_787 = layer_norm(axes = input_787_axes_0, beta = encoder_layers_14_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_14_norm_feed_forward2_weight, x = input_785)[name = tensor<string, []>("input_787")];
            tensor<fp32, [1, 188, 4096]> input_789 = linear(bias = encoder_layers_14_feed_forward2_linear1_bias, weight = encoder_layers_14_feed_forward2_linear1_weight_quantized, x = input_787)[name = tensor<string, []>("linear_134")];
            tensor<fp32, [1, 188, 4096]> input_791 = silu(x = input_789)[name = tensor<string, []>("input_791")];
            tensor<fp32, [1, 188, 1024]> input_795 = linear(bias = encoder_layers_14_feed_forward2_linear2_bias, weight = encoder_layers_14_feed_forward2_linear2_weight_quantized, x = input_791)[name = tensor<string, []>("linear_135")];
            tensor<fp32, []> var_2817 = const()[name = tensor<string, []>("op_2817"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2818 = mul(x = input_795, y = var_2817)[name = tensor<string, []>("op_2818")];
            tensor<fp32, [1, 188, 1024]> input_797 = add(x = input_785, y = var_2818)[name = tensor<string, []>("input_797")];
            tensor<int32, [1]> input_799_axes_0 = const()[name = tensor<string, []>("input_799_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_799 = layer_norm(axes = input_799_axes_0, beta = encoder_layers_14_norm_out_bias, epsilon = var_11, gamma = encoder_layers_14_norm_out_weight, x = input_797)[name = tensor<string, []>("input_799")];
            tensor<int32, [1]> input_801_axes_0 = const()[name = tensor<string, []>("input_801_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_801 = layer_norm(axes = input_801_axes_0, beta = encoder_layers_15_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_15_norm_feed_forward1_weight, x = input_799)[name = tensor<string, []>("input_801")];
            tensor<fp32, [1, 188, 4096]> input_803 = linear(bias = encoder_layers_15_feed_forward1_linear1_bias, weight = encoder_layers_15_feed_forward1_linear1_weight_quantized, x = input_801)[name = tensor<string, []>("linear_136")];
            tensor<fp32, [1, 188, 4096]> input_805 = silu(x = input_803)[name = tensor<string, []>("input_805")];
            tensor<fp32, [1, 188, 1024]> input_809 = linear(bias = encoder_layers_15_feed_forward1_linear2_bias, weight = encoder_layers_15_feed_forward1_linear2_weight_quantized, x = input_805)[name = tensor<string, []>("linear_137")];
            tensor<fp32, []> var_2848 = const()[name = tensor<string, []>("op_2848"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2849 = mul(x = input_809, y = var_2848)[name = tensor<string, []>("op_2849")];
            tensor<fp32, [1, 188, 1024]> input_811 = add(x = input_799, y = var_2849)[name = tensor<string, []>("input_811")];
            tensor<int32, [1]> query_31_axes_0 = const()[name = tensor<string, []>("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_31 = layer_norm(axes = query_31_axes_0, beta = encoder_layers_15_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_15_norm_self_att_weight, x = input_811)[name = tensor<string, []>("query_31")];
            tensor<fp32, [1, 188, 1024]> var_2865 = linear(bias = encoder_layers_15_self_attn_linear_q_bias, weight = encoder_layers_15_self_attn_linear_q_weight_quantized, x = query_31)[name = tensor<string, []>("linear_138")];
            tensor<int32, [4]> var_2866 = const()[name = tensor<string, []>("op_2866"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_91 = reshape(shape = var_2866, x = var_2865)[name = tensor<string, []>("q_91")];
            tensor<fp32, [1, 188, 1024]> var_2870 = linear(bias = encoder_layers_15_self_attn_linear_k_bias, weight = encoder_layers_15_self_attn_linear_k_weight_quantized, x = query_31)[name = tensor<string, []>("linear_139")];
            tensor<int32, [4]> var_2871 = const()[name = tensor<string, []>("op_2871"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_61 = reshape(shape = var_2871, x = var_2870)[name = tensor<string, []>("k_61")];
            tensor<fp32, [1, 188, 1024]> var_2875 = linear(bias = encoder_layers_15_self_attn_linear_v_bias, weight = encoder_layers_15_self_attn_linear_v_weight_quantized, x = query_31)[name = tensor<string, []>("linear_140")];
            tensor<int32, [4]> var_2876 = const()[name = tensor<string, []>("op_2876"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_31 = reshape(shape = var_2876, x = var_2875)[name = tensor<string, []>("v_31")];
            tensor<int32, [4]> value_33_perm_0 = const()[name = tensor<string, []>("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_2888 = add(x = q_91, y = encoder_layers_15_self_attn_pos_bias_u)[name = tensor<string, []>("op_2888")];
            tensor<fp32, [1, 188, 8, 128]> var_2890 = add(x = q_91, y = encoder_layers_15_self_attn_pos_bias_v)[name = tensor<string, []>("op_2890")];
            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_2892_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2892_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593746624))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594131136))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594130688)))];
            tensor<bool, []> x_339_transpose_x_0 = const()[name = tensor<string, []>("x_339_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_339_transpose_y_0 = const()[name = tensor<string, []>("x_339_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_31 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2890)[name = tensor<string, []>("transpose_206")];
            tensor<fp32, [1, 8, 188, 375]> x_339 = matmul(transpose_x = x_339_transpose_x_0, transpose_y = x_339_transpose_y_0, x = q_with_bias_v_31, y = op_2892_quantized)[name = tensor<string, []>("x_339")];
            tensor<fp32, []> const_164 = const()[name = tensor<string, []>("const_164"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_341_pad_0 = const()[name = tensor<string, []>("x_341_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_341_mode_0 = const()[name = tensor<string, []>("x_341_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_341 = pad(constant_val = const_164, mode = x_341_mode_0, pad = x_341_pad_0, x = x_339)[name = tensor<string, []>("x_341")];
            tensor<int32, [4]> var_2900 = const()[name = tensor<string, []>("op_2900"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_343 = reshape(shape = var_2900, x = x_341)[name = tensor<string, []>("x_343")];
            tensor<int32, [4]> var_2904_begin_0 = const()[name = tensor<string, []>("op_2904_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2904_end_0 = const()[name = tensor<string, []>("op_2904_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2904_end_mask_0 = const()[name = tensor<string, []>("op_2904_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_2904 = slice_by_index(begin = var_2904_begin_0, end = var_2904_end_0, end_mask = var_2904_end_mask_0, x = x_343)[name = tensor<string, []>("op_2904")];
            tensor<int32, [4]> var_2905 = const()[name = tensor<string, []>("op_2905"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_61 = reshape(shape = var_2905, x = var_2904)[name = tensor<string, []>("matrix_bd_61")];
            tensor<bool, []> matrix_ac_31_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_31_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_126_perm_0 = const()[name = tensor<string, []>("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_127_perm_0 = const()[name = tensor<string, []>("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61)[name = tensor<string, []>("transpose_204")];
            tensor<fp32, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2888)[name = tensor<string, []>("transpose_205")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_31 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = tensor<string, []>("matrix_ac_31")];
            tensor<int32, [4]> matrix_bd_63_begin_0 = const()[name = tensor<string, []>("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = tensor<string, []>("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_63 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61)[name = tensor<string, []>("matrix_bd_63")];
            tensor<fp32, [1, 8, 188, 188]> var_2914 = add(x = matrix_ac_31, y = matrix_bd_63)[name = tensor<string, []>("op_2914")];
            tensor<fp32, []> _inversed_scores_61_y_0 = const()[name = tensor<string, []>("_inversed_scores_61_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_61 = mul(x = var_2914, y = _inversed_scores_61_y_0)[name = tensor<string, []>("_inversed_scores_61")];
            tensor<fp32, [1, 8, 188, 188]> scores_63 = select(a = var_14, b = _inversed_scores_61, cond = mask_3)[name = tensor<string, []>("scores_63")];
            tensor<fp32, [1, 8, 188, 188]> var_2920 = softmax(axis = var_32, x = scores_63)[name = tensor<string, []>("op_2920")];
            tensor<fp32, [1, 8, 188, 188]> input_813 = select(a = var_13, b = var_2920, cond = mask_3)[name = tensor<string, []>("input_813")];
            tensor<bool, []> x_345_transpose_x_0 = const()[name = tensor<string, []>("x_345_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_345_transpose_y_0 = const()[name = tensor<string, []>("x_345_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_33 = transpose(perm = value_33_perm_0, x = v_31)[name = tensor<string, []>("transpose_203")];
            tensor<fp32, [1, 8, 188, 128]> x_345 = matmul(transpose_x = x_345_transpose_x_0, transpose_y = x_345_transpose_y_0, x = input_813, y = value_33)[name = tensor<string, []>("x_345")];
            tensor<int32, [4]> var_2924_perm_0 = const()[name = tensor<string, []>("op_2924_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2925 = const()[name = tensor<string, []>("op_2925"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_2924 = transpose(perm = var_2924_perm_0, x = x_345)[name = tensor<string, []>("transpose_202")];
            tensor<fp32, [1, 188, 1024]> input_815 = reshape(shape = var_2925, x = var_2924)[name = tensor<string, []>("input_815")];
            tensor<fp32, [1, 188, 1024]> input_817 = linear(bias = encoder_layers_15_self_attn_linear_out_bias, weight = encoder_layers_15_self_attn_linear_out_weight_quantized, x = input_815)[name = tensor<string, []>("linear_142")];
            tensor<fp32, [1, 188, 1024]> input_819 = add(x = input_811, y = input_817)[name = tensor<string, []>("input_819")];
            tensor<int32, [1]> x_349_axes_0 = const()[name = tensor<string, []>("x_349_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_349 = layer_norm(axes = x_349_axes_0, beta = encoder_layers_15_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_15_norm_conv_weight, x = input_819)[name = tensor<string, []>("x_349")];
            tensor<int32, [3]> input_821_perm_0 = const()[name = tensor<string, []>("input_821_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_823_pad_type_0 = const()[name = tensor<string, []>("input_823_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_823_strides_0 = const()[name = tensor<string, []>("input_823_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_823_pad_0 = const()[name = tensor<string, []>("input_823_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_823_dilations_0 = const()[name = tensor<string, []>("input_823_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_823_groups_0 = const()[name = tensor<string, []>("input_823_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_821 = transpose(perm = input_821_perm_0, x = x_349)[name = tensor<string, []>("transpose_201")];
            tensor<fp32, [1, 2048, 188]> input_823 = conv(bias = encoder_layers_15_conv_pointwise_conv1_bias, dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = encoder_layers_15_conv_pointwise_conv1_weight_quantized, x = input_821)[name = tensor<string, []>("input_823")];
            tensor<int32, []> x_351_split_num_splits_0 = const()[name = tensor<string, []>("x_351_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_351_split_axis_0 = const()[name = tensor<string, []>("x_351_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_351_split_0, tensor<fp32, [1, 1024, 188]> x_351_split_1 = split(axis = x_351_split_axis_0, num_splits = x_351_split_num_splits_0, x = input_823)[name = tensor<string, []>("x_351_split")];
            tensor<fp32, [1, 1024, 188]> x_351_split_1_sigmoid = sigmoid(x = x_351_split_1)[name = tensor<string, []>("x_351_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_351 = mul(x = x_351_split_0, y = x_351_split_1_sigmoid)[name = tensor<string, []>("x_351")];
            tensor<fp32, [1, 1024, 188]> input_825 = select(a = var_13, b = x_351, cond = var_339)[name = tensor<string, []>("input_825")];
            tensor<fp32, []> const_167 = const()[name = tensor<string, []>("const_167"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_827_pad_0 = const()[name = tensor<string, []>("input_827_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_827_mode_0 = const()[name = tensor<string, []>("input_827_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_827 = pad(constant_val = const_167, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825)[name = tensor<string, []>("input_827")];
            tensor<string, []> input_829_pad_type_0 = const()[name = tensor<string, []>("input_829_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_829_groups_0 = const()[name = tensor<string, []>("input_829_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_829_strides_0 = const()[name = tensor<string, []>("input_829_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_829_pad_0 = const()[name = tensor<string, []>("input_829_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_829_dilations_0 = const()[name = tensor<string, []>("input_829_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_278_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_278_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594132736))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594143104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594142016)))];
            tensor<fp32, [1024]> const_279 = const()[name = tensor<string, []>("const_279"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594147264)))];
            tensor<fp32, [1, 1024, 188]> input_831 = conv(bias = const_279, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_quantized, x = input_827)[name = tensor<string, []>("input_831")];
            tensor<fp32, [1, 1024, 188]> input_833 = silu(x = input_831)[name = tensor<string, []>("input_833")];
            tensor<string, []> x_353_pad_type_0 = const()[name = tensor<string, []>("x_353_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_353_strides_0 = const()[name = tensor<string, []>("x_353_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_353_pad_0 = const()[name = tensor<string, []>("x_353_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_353_dilations_0 = const()[name = tensor<string, []>("x_353_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_353_groups_0 = const()[name = tensor<string, []>("x_353_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_353 = conv(bias = encoder_layers_15_conv_pointwise_conv2_bias, dilations = x_353_dilations_0, groups = x_353_groups_0, pad = x_353_pad_0, pad_type = x_353_pad_type_0, strides = x_353_strides_0, weight = encoder_layers_15_conv_pointwise_conv2_weight_quantized, x = input_833)[name = tensor<string, []>("x_353")];
            tensor<int32, [3]> input_835_perm_0 = const()[name = tensor<string, []>("input_835_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_835 = transpose(perm = input_835_perm_0, x = x_353)[name = tensor<string, []>("transpose_200")];
            tensor<fp32, [1, 188, 1024]> input_837 = add(x = input_819, y = input_835)[name = tensor<string, []>("input_837")];
            tensor<int32, [1]> input_839_axes_0 = const()[name = tensor<string, []>("input_839_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_839 = layer_norm(axes = input_839_axes_0, beta = encoder_layers_15_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_15_norm_feed_forward2_weight, x = input_837)[name = tensor<string, []>("input_839")];
            tensor<fp32, [1, 188, 4096]> input_841 = linear(bias = encoder_layers_15_feed_forward2_linear1_bias, weight = encoder_layers_15_feed_forward2_linear1_weight_quantized, x = input_839)[name = tensor<string, []>("linear_143")];
            tensor<fp32, [1, 188, 4096]> input_843 = silu(x = input_841)[name = tensor<string, []>("input_843")];
            tensor<fp32, [1, 188, 1024]> input_847 = linear(bias = encoder_layers_15_feed_forward2_linear2_bias, weight = encoder_layers_15_feed_forward2_linear2_weight_quantized, x = input_843)[name = tensor<string, []>("linear_144")];
            tensor<fp32, []> var_2991 = const()[name = tensor<string, []>("op_2991"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_2992 = mul(x = input_847, y = var_2991)[name = tensor<string, []>("op_2992")];
            tensor<fp32, [1, 188, 1024]> input_849 = add(x = input_837, y = var_2992)[name = tensor<string, []>("input_849")];
            tensor<int32, [1]> input_851_axes_0 = const()[name = tensor<string, []>("input_851_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_851 = layer_norm(axes = input_851_axes_0, beta = encoder_layers_15_norm_out_bias, epsilon = var_11, gamma = encoder_layers_15_norm_out_weight, x = input_849)[name = tensor<string, []>("input_851")];
            tensor<int32, [1]> input_853_axes_0 = const()[name = tensor<string, []>("input_853_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_853 = layer_norm(axes = input_853_axes_0, beta = encoder_layers_16_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_16_norm_feed_forward1_weight, x = input_851)[name = tensor<string, []>("input_853")];
            tensor<fp32, [1, 188, 4096]> input_855 = linear(bias = encoder_layers_16_feed_forward1_linear1_bias, weight = encoder_layers_16_feed_forward1_linear1_weight_quantized, x = input_853)[name = tensor<string, []>("linear_145")];
            tensor<fp32, [1, 188, 4096]> input_857 = silu(x = input_855)[name = tensor<string, []>("input_857")];
            tensor<fp32, [1, 188, 1024]> input_861 = linear(bias = encoder_layers_16_feed_forward1_linear2_bias, weight = encoder_layers_16_feed_forward1_linear2_weight_quantized, x = input_857)[name = tensor<string, []>("linear_146")];
            tensor<fp32, []> var_3022 = const()[name = tensor<string, []>("op_3022"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3023 = mul(x = input_861, y = var_3022)[name = tensor<string, []>("op_3023")];
            tensor<fp32, [1, 188, 1024]> input_863 = add(x = input_851, y = var_3023)[name = tensor<string, []>("input_863")];
            tensor<int32, [1]> query_33_axes_0 = const()[name = tensor<string, []>("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_33 = layer_norm(axes = query_33_axes_0, beta = encoder_layers_16_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_16_norm_self_att_weight, x = input_863)[name = tensor<string, []>("query_33")];
            tensor<fp32, [1, 188, 1024]> var_3039 = linear(bias = encoder_layers_16_self_attn_linear_q_bias, weight = encoder_layers_16_self_attn_linear_q_weight_quantized, x = query_33)[name = tensor<string, []>("linear_147")];
            tensor<int32, [4]> var_3040 = const()[name = tensor<string, []>("op_3040"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_97 = reshape(shape = var_3040, x = var_3039)[name = tensor<string, []>("q_97")];
            tensor<fp32, [1, 188, 1024]> var_3044 = linear(bias = encoder_layers_16_self_attn_linear_k_bias, weight = encoder_layers_16_self_attn_linear_k_weight_quantized, x = query_33)[name = tensor<string, []>("linear_148")];
            tensor<int32, [4]> var_3045 = const()[name = tensor<string, []>("op_3045"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_65 = reshape(shape = var_3045, x = var_3044)[name = tensor<string, []>("k_65")];
            tensor<fp32, [1, 188, 1024]> var_3049 = linear(bias = encoder_layers_16_self_attn_linear_v_bias, weight = encoder_layers_16_self_attn_linear_v_weight_quantized, x = query_33)[name = tensor<string, []>("linear_149")];
            tensor<int32, [4]> var_3050 = const()[name = tensor<string, []>("op_3050"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_33 = reshape(shape = var_3050, x = var_3049)[name = tensor<string, []>("v_33")];
            tensor<int32, [4]> value_35_perm_0 = const()[name = tensor<string, []>("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_3062 = add(x = q_97, y = encoder_layers_16_self_attn_pos_bias_u)[name = tensor<string, []>("op_3062")];
            tensor<fp32, [1, 188, 8, 128]> var_3064 = add(x = q_97, y = encoder_layers_16_self_attn_pos_bias_v)[name = tensor<string, []>("op_3064")];
            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_3066_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3066_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594151424))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594535936))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594535488)))];
            tensor<bool, []> x_361_transpose_x_0 = const()[name = tensor<string, []>("x_361_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_361_transpose_y_0 = const()[name = tensor<string, []>("x_361_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_33 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3064)[name = tensor<string, []>("transpose_199")];
            tensor<fp32, [1, 8, 188, 375]> x_361 = matmul(transpose_x = x_361_transpose_x_0, transpose_y = x_361_transpose_y_0, x = q_with_bias_v_33, y = op_3066_quantized)[name = tensor<string, []>("x_361")];
            tensor<fp32, []> const_174 = const()[name = tensor<string, []>("const_174"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_363_pad_0 = const()[name = tensor<string, []>("x_363_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_363_mode_0 = const()[name = tensor<string, []>("x_363_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_363 = pad(constant_val = const_174, mode = x_363_mode_0, pad = x_363_pad_0, x = x_361)[name = tensor<string, []>("x_363")];
            tensor<int32, [4]> var_3074 = const()[name = tensor<string, []>("op_3074"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_365 = reshape(shape = var_3074, x = x_363)[name = tensor<string, []>("x_365")];
            tensor<int32, [4]> var_3078_begin_0 = const()[name = tensor<string, []>("op_3078_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3078_end_0 = const()[name = tensor<string, []>("op_3078_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3078_end_mask_0 = const()[name = tensor<string, []>("op_3078_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_3078 = slice_by_index(begin = var_3078_begin_0, end = var_3078_end_0, end_mask = var_3078_end_mask_0, x = x_365)[name = tensor<string, []>("op_3078")];
            tensor<int32, [4]> var_3079 = const()[name = tensor<string, []>("op_3079"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_65 = reshape(shape = var_3079, x = var_3078)[name = tensor<string, []>("matrix_bd_65")];
            tensor<bool, []> matrix_ac_33_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_33_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_33_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_33_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_128_perm_0 = const()[name = tensor<string, []>("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_129_perm_0 = const()[name = tensor<string, []>("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65)[name = tensor<string, []>("transpose_197")];
            tensor<fp32, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3062)[name = tensor<string, []>("transpose_198")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_33 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = tensor<string, []>("matrix_ac_33")];
            tensor<int32, [4]> matrix_bd_67_begin_0 = const()[name = tensor<string, []>("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = tensor<string, []>("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_67 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65)[name = tensor<string, []>("matrix_bd_67")];
            tensor<fp32, [1, 8, 188, 188]> var_3088 = add(x = matrix_ac_33, y = matrix_bd_67)[name = tensor<string, []>("op_3088")];
            tensor<fp32, []> _inversed_scores_65_y_0 = const()[name = tensor<string, []>("_inversed_scores_65_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_65 = mul(x = var_3088, y = _inversed_scores_65_y_0)[name = tensor<string, []>("_inversed_scores_65")];
            tensor<fp32, [1, 8, 188, 188]> scores_67 = select(a = var_14, b = _inversed_scores_65, cond = mask_3)[name = tensor<string, []>("scores_67")];
            tensor<fp32, [1, 8, 188, 188]> var_3094 = softmax(axis = var_32, x = scores_67)[name = tensor<string, []>("op_3094")];
            tensor<fp32, [1, 8, 188, 188]> input_865 = select(a = var_13, b = var_3094, cond = mask_3)[name = tensor<string, []>("input_865")];
            tensor<bool, []> x_367_transpose_x_0 = const()[name = tensor<string, []>("x_367_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_367_transpose_y_0 = const()[name = tensor<string, []>("x_367_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_35 = transpose(perm = value_35_perm_0, x = v_33)[name = tensor<string, []>("transpose_196")];
            tensor<fp32, [1, 8, 188, 128]> x_367 = matmul(transpose_x = x_367_transpose_x_0, transpose_y = x_367_transpose_y_0, x = input_865, y = value_35)[name = tensor<string, []>("x_367")];
            tensor<int32, [4]> var_3098_perm_0 = const()[name = tensor<string, []>("op_3098_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3099 = const()[name = tensor<string, []>("op_3099"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_3098 = transpose(perm = var_3098_perm_0, x = x_367)[name = tensor<string, []>("transpose_195")];
            tensor<fp32, [1, 188, 1024]> input_867 = reshape(shape = var_3099, x = var_3098)[name = tensor<string, []>("input_867")];
            tensor<fp32, [1, 188, 1024]> input_869 = linear(bias = encoder_layers_16_self_attn_linear_out_bias, weight = encoder_layers_16_self_attn_linear_out_weight_quantized, x = input_867)[name = tensor<string, []>("linear_151")];
            tensor<fp32, [1, 188, 1024]> input_871 = add(x = input_863, y = input_869)[name = tensor<string, []>("input_871")];
            tensor<int32, [1]> x_371_axes_0 = const()[name = tensor<string, []>("x_371_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_371 = layer_norm(axes = x_371_axes_0, beta = encoder_layers_16_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_16_norm_conv_weight, x = input_871)[name = tensor<string, []>("x_371")];
            tensor<int32, [3]> input_873_perm_0 = const()[name = tensor<string, []>("input_873_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_875_pad_type_0 = const()[name = tensor<string, []>("input_875_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_875_strides_0 = const()[name = tensor<string, []>("input_875_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_875_pad_0 = const()[name = tensor<string, []>("input_875_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_875_dilations_0 = const()[name = tensor<string, []>("input_875_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_875_groups_0 = const()[name = tensor<string, []>("input_875_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_873 = transpose(perm = input_873_perm_0, x = x_371)[name = tensor<string, []>("transpose_194")];
            tensor<fp32, [1, 2048, 188]> input_875 = conv(bias = encoder_layers_16_conv_pointwise_conv1_bias, dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = encoder_layers_16_conv_pointwise_conv1_weight_quantized, x = input_873)[name = tensor<string, []>("input_875")];
            tensor<int32, []> x_373_split_num_splits_0 = const()[name = tensor<string, []>("x_373_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_373_split_axis_0 = const()[name = tensor<string, []>("x_373_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_373_split_0, tensor<fp32, [1, 1024, 188]> x_373_split_1 = split(axis = x_373_split_axis_0, num_splits = x_373_split_num_splits_0, x = input_875)[name = tensor<string, []>("x_373_split")];
            tensor<fp32, [1, 1024, 188]> x_373_split_1_sigmoid = sigmoid(x = x_373_split_1)[name = tensor<string, []>("x_373_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_373 = mul(x = x_373_split_0, y = x_373_split_1_sigmoid)[name = tensor<string, []>("x_373")];
            tensor<fp32, [1, 1024, 188]> input_877 = select(a = var_13, b = x_373, cond = var_339)[name = tensor<string, []>("input_877")];
            tensor<fp32, []> const_177 = const()[name = tensor<string, []>("const_177"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_879_pad_0 = const()[name = tensor<string, []>("input_879_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_879_mode_0 = const()[name = tensor<string, []>("input_879_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_879 = pad(constant_val = const_177, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877)[name = tensor<string, []>("input_879")];
            tensor<string, []> input_881_pad_type_0 = const()[name = tensor<string, []>("input_881_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_881_groups_0 = const()[name = tensor<string, []>("input_881_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_881_strides_0 = const()[name = tensor<string, []>("input_881_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_881_pad_0 = const()[name = tensor<string, []>("input_881_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_881_dilations_0 = const()[name = tensor<string, []>("input_881_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_280_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_280_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594537536))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594547904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594546816)))];
            tensor<fp32, [1024]> const_281 = const()[name = tensor<string, []>("const_281"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594552064)))];
            tensor<fp32, [1, 1024, 188]> input_883 = conv(bias = const_281, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_quantized, x = input_879)[name = tensor<string, []>("input_883")];
            tensor<fp32, [1, 1024, 188]> input_885 = silu(x = input_883)[name = tensor<string, []>("input_885")];
            tensor<string, []> x_375_pad_type_0 = const()[name = tensor<string, []>("x_375_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_375_strides_0 = const()[name = tensor<string, []>("x_375_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_375_pad_0 = const()[name = tensor<string, []>("x_375_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_375_dilations_0 = const()[name = tensor<string, []>("x_375_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_375_groups_0 = const()[name = tensor<string, []>("x_375_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_375 = conv(bias = encoder_layers_16_conv_pointwise_conv2_bias, dilations = x_375_dilations_0, groups = x_375_groups_0, pad = x_375_pad_0, pad_type = x_375_pad_type_0, strides = x_375_strides_0, weight = encoder_layers_16_conv_pointwise_conv2_weight_quantized, x = input_885)[name = tensor<string, []>("x_375")];
            tensor<int32, [3]> input_887_perm_0 = const()[name = tensor<string, []>("input_887_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_887 = transpose(perm = input_887_perm_0, x = x_375)[name = tensor<string, []>("transpose_193")];
            tensor<fp32, [1, 188, 1024]> input_889 = add(x = input_871, y = input_887)[name = tensor<string, []>("input_889")];
            tensor<int32, [1]> input_891_axes_0 = const()[name = tensor<string, []>("input_891_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_891 = layer_norm(axes = input_891_axes_0, beta = encoder_layers_16_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_16_norm_feed_forward2_weight, x = input_889)[name = tensor<string, []>("input_891")];
            tensor<fp32, [1, 188, 4096]> input_893 = linear(bias = encoder_layers_16_feed_forward2_linear1_bias, weight = encoder_layers_16_feed_forward2_linear1_weight_quantized, x = input_891)[name = tensor<string, []>("linear_152")];
            tensor<fp32, [1, 188, 4096]> input_895 = silu(x = input_893)[name = tensor<string, []>("input_895")];
            tensor<fp32, [1, 188, 1024]> input_899 = linear(bias = encoder_layers_16_feed_forward2_linear2_bias, weight = encoder_layers_16_feed_forward2_linear2_weight_quantized, x = input_895)[name = tensor<string, []>("linear_153")];
            tensor<fp32, []> var_3165 = const()[name = tensor<string, []>("op_3165"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3166 = mul(x = input_899, y = var_3165)[name = tensor<string, []>("op_3166")];
            tensor<fp32, [1, 188, 1024]> input_901 = add(x = input_889, y = var_3166)[name = tensor<string, []>("input_901")];
            tensor<int32, [1]> input_903_axes_0 = const()[name = tensor<string, []>("input_903_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_903 = layer_norm(axes = input_903_axes_0, beta = encoder_layers_16_norm_out_bias, epsilon = var_11, gamma = encoder_layers_16_norm_out_weight, x = input_901)[name = tensor<string, []>("input_903")];
            tensor<int32, [1]> input_905_axes_0 = const()[name = tensor<string, []>("input_905_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_905 = layer_norm(axes = input_905_axes_0, beta = encoder_layers_17_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_17_norm_feed_forward1_weight, x = input_903)[name = tensor<string, []>("input_905")];
            tensor<fp32, [1, 188, 4096]> input_907 = linear(bias = encoder_layers_17_feed_forward1_linear1_bias, weight = encoder_layers_17_feed_forward1_linear1_weight_quantized, x = input_905)[name = tensor<string, []>("linear_154")];
            tensor<fp32, [1, 188, 4096]> input_909 = silu(x = input_907)[name = tensor<string, []>("input_909")];
            tensor<fp32, [1, 188, 1024]> input_913 = linear(bias = encoder_layers_17_feed_forward1_linear2_bias, weight = encoder_layers_17_feed_forward1_linear2_weight_quantized, x = input_909)[name = tensor<string, []>("linear_155")];
            tensor<fp32, []> var_3196 = const()[name = tensor<string, []>("op_3196"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3197 = mul(x = input_913, y = var_3196)[name = tensor<string, []>("op_3197")];
            tensor<fp32, [1, 188, 1024]> input_915 = add(x = input_903, y = var_3197)[name = tensor<string, []>("input_915")];
            tensor<int32, [1]> query_35_axes_0 = const()[name = tensor<string, []>("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_35 = layer_norm(axes = query_35_axes_0, beta = encoder_layers_17_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_17_norm_self_att_weight, x = input_915)[name = tensor<string, []>("query_35")];
            tensor<fp32, [1, 188, 1024]> var_3213 = linear(bias = encoder_layers_17_self_attn_linear_q_bias, weight = encoder_layers_17_self_attn_linear_q_weight_quantized, x = query_35)[name = tensor<string, []>("linear_156")];
            tensor<int32, [4]> var_3214 = const()[name = tensor<string, []>("op_3214"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_103 = reshape(shape = var_3214, x = var_3213)[name = tensor<string, []>("q_103")];
            tensor<fp32, [1, 188, 1024]> var_3218 = linear(bias = encoder_layers_17_self_attn_linear_k_bias, weight = encoder_layers_17_self_attn_linear_k_weight_quantized, x = query_35)[name = tensor<string, []>("linear_157")];
            tensor<int32, [4]> var_3219 = const()[name = tensor<string, []>("op_3219"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_69 = reshape(shape = var_3219, x = var_3218)[name = tensor<string, []>("k_69")];
            tensor<fp32, [1, 188, 1024]> var_3223 = linear(bias = encoder_layers_17_self_attn_linear_v_bias, weight = encoder_layers_17_self_attn_linear_v_weight_quantized, x = query_35)[name = tensor<string, []>("linear_158")];
            tensor<int32, [4]> var_3224 = const()[name = tensor<string, []>("op_3224"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_35 = reshape(shape = var_3224, x = var_3223)[name = tensor<string, []>("v_35")];
            tensor<int32, [4]> value_37_perm_0 = const()[name = tensor<string, []>("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_3236 = add(x = q_103, y = encoder_layers_17_self_attn_pos_bias_u)[name = tensor<string, []>("op_3236")];
            tensor<fp32, [1, 188, 8, 128]> var_3238 = add(x = q_103, y = encoder_layers_17_self_attn_pos_bias_v)[name = tensor<string, []>("op_3238")];
            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_3240_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3240_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594556224))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594940736))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594940288)))];
            tensor<bool, []> x_383_transpose_x_0 = const()[name = tensor<string, []>("x_383_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_383_transpose_y_0 = const()[name = tensor<string, []>("x_383_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_35 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3238)[name = tensor<string, []>("transpose_192")];
            tensor<fp32, [1, 8, 188, 375]> x_383 = matmul(transpose_x = x_383_transpose_x_0, transpose_y = x_383_transpose_y_0, x = q_with_bias_v_35, y = op_3240_quantized)[name = tensor<string, []>("x_383")];
            tensor<fp32, []> const_184 = const()[name = tensor<string, []>("const_184"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_385_pad_0 = const()[name = tensor<string, []>("x_385_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_385_mode_0 = const()[name = tensor<string, []>("x_385_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_385 = pad(constant_val = const_184, mode = x_385_mode_0, pad = x_385_pad_0, x = x_383)[name = tensor<string, []>("x_385")];
            tensor<int32, [4]> var_3248 = const()[name = tensor<string, []>("op_3248"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_387 = reshape(shape = var_3248, x = x_385)[name = tensor<string, []>("x_387")];
            tensor<int32, [4]> var_3252_begin_0 = const()[name = tensor<string, []>("op_3252_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3252_end_0 = const()[name = tensor<string, []>("op_3252_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3252_end_mask_0 = const()[name = tensor<string, []>("op_3252_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_3252 = slice_by_index(begin = var_3252_begin_0, end = var_3252_end_0, end_mask = var_3252_end_mask_0, x = x_387)[name = tensor<string, []>("op_3252")];
            tensor<int32, [4]> var_3253 = const()[name = tensor<string, []>("op_3253"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_69 = reshape(shape = var_3253, x = var_3252)[name = tensor<string, []>("matrix_bd_69")];
            tensor<bool, []> matrix_ac_35_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_35_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_35_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_35_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_130_perm_0 = const()[name = tensor<string, []>("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_131_perm_0 = const()[name = tensor<string, []>("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69)[name = tensor<string, []>("transpose_190")];
            tensor<fp32, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3236)[name = tensor<string, []>("transpose_191")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_35 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = tensor<string, []>("matrix_ac_35")];
            tensor<int32, [4]> matrix_bd_71_begin_0 = const()[name = tensor<string, []>("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = tensor<string, []>("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_71 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69)[name = tensor<string, []>("matrix_bd_71")];
            tensor<fp32, [1, 8, 188, 188]> var_3262 = add(x = matrix_ac_35, y = matrix_bd_71)[name = tensor<string, []>("op_3262")];
            tensor<fp32, []> _inversed_scores_69_y_0 = const()[name = tensor<string, []>("_inversed_scores_69_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_69 = mul(x = var_3262, y = _inversed_scores_69_y_0)[name = tensor<string, []>("_inversed_scores_69")];
            tensor<fp32, [1, 8, 188, 188]> scores_71 = select(a = var_14, b = _inversed_scores_69, cond = mask_3)[name = tensor<string, []>("scores_71")];
            tensor<fp32, [1, 8, 188, 188]> var_3268 = softmax(axis = var_32, x = scores_71)[name = tensor<string, []>("op_3268")];
            tensor<fp32, [1, 8, 188, 188]> input_917 = select(a = var_13, b = var_3268, cond = mask_3)[name = tensor<string, []>("input_917")];
            tensor<bool, []> x_389_transpose_x_0 = const()[name = tensor<string, []>("x_389_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_389_transpose_y_0 = const()[name = tensor<string, []>("x_389_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_37 = transpose(perm = value_37_perm_0, x = v_35)[name = tensor<string, []>("transpose_189")];
            tensor<fp32, [1, 8, 188, 128]> x_389 = matmul(transpose_x = x_389_transpose_x_0, transpose_y = x_389_transpose_y_0, x = input_917, y = value_37)[name = tensor<string, []>("x_389")];
            tensor<int32, [4]> var_3272_perm_0 = const()[name = tensor<string, []>("op_3272_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3273 = const()[name = tensor<string, []>("op_3273"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_3272 = transpose(perm = var_3272_perm_0, x = x_389)[name = tensor<string, []>("transpose_188")];
            tensor<fp32, [1, 188, 1024]> input_919 = reshape(shape = var_3273, x = var_3272)[name = tensor<string, []>("input_919")];
            tensor<fp32, [1, 188, 1024]> input_921 = linear(bias = encoder_layers_17_self_attn_linear_out_bias, weight = encoder_layers_17_self_attn_linear_out_weight_quantized, x = input_919)[name = tensor<string, []>("linear_160")];
            tensor<fp32, [1, 188, 1024]> input_923 = add(x = input_915, y = input_921)[name = tensor<string, []>("input_923")];
            tensor<int32, [1]> x_393_axes_0 = const()[name = tensor<string, []>("x_393_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_393 = layer_norm(axes = x_393_axes_0, beta = encoder_layers_17_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_17_norm_conv_weight, x = input_923)[name = tensor<string, []>("x_393")];
            tensor<int32, [3]> input_925_perm_0 = const()[name = tensor<string, []>("input_925_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_927_pad_type_0 = const()[name = tensor<string, []>("input_927_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_927_strides_0 = const()[name = tensor<string, []>("input_927_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_927_pad_0 = const()[name = tensor<string, []>("input_927_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_927_dilations_0 = const()[name = tensor<string, []>("input_927_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_927_groups_0 = const()[name = tensor<string, []>("input_927_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_925 = transpose(perm = input_925_perm_0, x = x_393)[name = tensor<string, []>("transpose_187")];
            tensor<fp32, [1, 2048, 188]> input_927 = conv(bias = encoder_layers_17_conv_pointwise_conv1_bias, dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = encoder_layers_17_conv_pointwise_conv1_weight_quantized, x = input_925)[name = tensor<string, []>("input_927")];
            tensor<int32, []> x_395_split_num_splits_0 = const()[name = tensor<string, []>("x_395_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_395_split_axis_0 = const()[name = tensor<string, []>("x_395_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_395_split_0, tensor<fp32, [1, 1024, 188]> x_395_split_1 = split(axis = x_395_split_axis_0, num_splits = x_395_split_num_splits_0, x = input_927)[name = tensor<string, []>("x_395_split")];
            tensor<fp32, [1, 1024, 188]> x_395_split_1_sigmoid = sigmoid(x = x_395_split_1)[name = tensor<string, []>("x_395_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_395 = mul(x = x_395_split_0, y = x_395_split_1_sigmoid)[name = tensor<string, []>("x_395")];
            tensor<fp32, [1, 1024, 188]> input_929 = select(a = var_13, b = x_395, cond = var_339)[name = tensor<string, []>("input_929")];
            tensor<fp32, []> const_187 = const()[name = tensor<string, []>("const_187"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_931_pad_0 = const()[name = tensor<string, []>("input_931_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_931_mode_0 = const()[name = tensor<string, []>("input_931_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_931 = pad(constant_val = const_187, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929)[name = tensor<string, []>("input_931")];
            tensor<string, []> input_933_pad_type_0 = const()[name = tensor<string, []>("input_933_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_933_groups_0 = const()[name = tensor<string, []>("input_933_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_933_strides_0 = const()[name = tensor<string, []>("input_933_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_933_pad_0 = const()[name = tensor<string, []>("input_933_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_933_dilations_0 = const()[name = tensor<string, []>("input_933_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_282_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_282_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594942336))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594952704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594951616)))];
            tensor<fp32, [1024]> const_283 = const()[name = tensor<string, []>("const_283"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594956864)))];
            tensor<fp32, [1, 1024, 188]> input_935 = conv(bias = const_283, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_quantized, x = input_931)[name = tensor<string, []>("input_935")];
            tensor<fp32, [1, 1024, 188]> input_937 = silu(x = input_935)[name = tensor<string, []>("input_937")];
            tensor<string, []> x_397_pad_type_0 = const()[name = tensor<string, []>("x_397_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_397_strides_0 = const()[name = tensor<string, []>("x_397_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_397_pad_0 = const()[name = tensor<string, []>("x_397_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_397_dilations_0 = const()[name = tensor<string, []>("x_397_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_397_groups_0 = const()[name = tensor<string, []>("x_397_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_397 = conv(bias = encoder_layers_17_conv_pointwise_conv2_bias, dilations = x_397_dilations_0, groups = x_397_groups_0, pad = x_397_pad_0, pad_type = x_397_pad_type_0, strides = x_397_strides_0, weight = encoder_layers_17_conv_pointwise_conv2_weight_quantized, x = input_937)[name = tensor<string, []>("x_397")];
            tensor<int32, [3]> input_939_perm_0 = const()[name = tensor<string, []>("input_939_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_939 = transpose(perm = input_939_perm_0, x = x_397)[name = tensor<string, []>("transpose_186")];
            tensor<fp32, [1, 188, 1024]> input_941 = add(x = input_923, y = input_939)[name = tensor<string, []>("input_941")];
            tensor<int32, [1]> input_943_axes_0 = const()[name = tensor<string, []>("input_943_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_943 = layer_norm(axes = input_943_axes_0, beta = encoder_layers_17_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_17_norm_feed_forward2_weight, x = input_941)[name = tensor<string, []>("input_943")];
            tensor<fp32, [1, 188, 4096]> input_945 = linear(bias = encoder_layers_17_feed_forward2_linear1_bias, weight = encoder_layers_17_feed_forward2_linear1_weight_quantized, x = input_943)[name = tensor<string, []>("linear_161")];
            tensor<fp32, [1, 188, 4096]> input_947 = silu(x = input_945)[name = tensor<string, []>("input_947")];
            tensor<fp32, [1, 188, 1024]> input_951 = linear(bias = encoder_layers_17_feed_forward2_linear2_bias, weight = encoder_layers_17_feed_forward2_linear2_weight_quantized, x = input_947)[name = tensor<string, []>("linear_162")];
            tensor<fp32, []> var_3339 = const()[name = tensor<string, []>("op_3339"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3340 = mul(x = input_951, y = var_3339)[name = tensor<string, []>("op_3340")];
            tensor<fp32, [1, 188, 1024]> input_953 = add(x = input_941, y = var_3340)[name = tensor<string, []>("input_953")];
            tensor<int32, [1]> input_955_axes_0 = const()[name = tensor<string, []>("input_955_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_955 = layer_norm(axes = input_955_axes_0, beta = encoder_layers_17_norm_out_bias, epsilon = var_11, gamma = encoder_layers_17_norm_out_weight, x = input_953)[name = tensor<string, []>("input_955")];
            tensor<int32, [1]> input_957_axes_0 = const()[name = tensor<string, []>("input_957_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_957 = layer_norm(axes = input_957_axes_0, beta = encoder_layers_18_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_18_norm_feed_forward1_weight, x = input_955)[name = tensor<string, []>("input_957")];
            tensor<fp32, [1, 188, 4096]> input_959 = linear(bias = encoder_layers_18_feed_forward1_linear1_bias, weight = encoder_layers_18_feed_forward1_linear1_weight_quantized, x = input_957)[name = tensor<string, []>("linear_163")];
            tensor<fp32, [1, 188, 4096]> input_961 = silu(x = input_959)[name = tensor<string, []>("input_961")];
            tensor<fp32, [1, 188, 1024]> input_965 = linear(bias = encoder_layers_18_feed_forward1_linear2_bias, weight = encoder_layers_18_feed_forward1_linear2_weight_quantized, x = input_961)[name = tensor<string, []>("linear_164")];
            tensor<fp32, []> var_3370 = const()[name = tensor<string, []>("op_3370"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3371 = mul(x = input_965, y = var_3370)[name = tensor<string, []>("op_3371")];
            tensor<fp32, [1, 188, 1024]> input_967 = add(x = input_955, y = var_3371)[name = tensor<string, []>("input_967")];
            tensor<int32, [1]> query_37_axes_0 = const()[name = tensor<string, []>("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_37 = layer_norm(axes = query_37_axes_0, beta = encoder_layers_18_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_18_norm_self_att_weight, x = input_967)[name = tensor<string, []>("query_37")];
            tensor<fp32, [1, 188, 1024]> var_3387 = linear(bias = encoder_layers_18_self_attn_linear_q_bias, weight = encoder_layers_18_self_attn_linear_q_weight_quantized, x = query_37)[name = tensor<string, []>("linear_165")];
            tensor<int32, [4]> var_3388 = const()[name = tensor<string, []>("op_3388"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_109 = reshape(shape = var_3388, x = var_3387)[name = tensor<string, []>("q_109")];
            tensor<fp32, [1, 188, 1024]> var_3392 = linear(bias = encoder_layers_18_self_attn_linear_k_bias, weight = encoder_layers_18_self_attn_linear_k_weight_quantized, x = query_37)[name = tensor<string, []>("linear_166")];
            tensor<int32, [4]> var_3393 = const()[name = tensor<string, []>("op_3393"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_73 = reshape(shape = var_3393, x = var_3392)[name = tensor<string, []>("k_73")];
            tensor<fp32, [1, 188, 1024]> var_3397 = linear(bias = encoder_layers_18_self_attn_linear_v_bias, weight = encoder_layers_18_self_attn_linear_v_weight_quantized, x = query_37)[name = tensor<string, []>("linear_167")];
            tensor<int32, [4]> var_3398 = const()[name = tensor<string, []>("op_3398"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_37 = reshape(shape = var_3398, x = var_3397)[name = tensor<string, []>("v_37")];
            tensor<int32, [4]> value_39_perm_0 = const()[name = tensor<string, []>("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_3410 = add(x = q_109, y = encoder_layers_18_self_attn_pos_bias_u)[name = tensor<string, []>("op_3410")];
            tensor<fp32, [1, 188, 8, 128]> var_3412 = add(x = q_109, y = encoder_layers_18_self_attn_pos_bias_v)[name = tensor<string, []>("op_3412")];
            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_3414_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3414_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594961024))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595345536))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595345088)))];
            tensor<bool, []> x_405_transpose_x_0 = const()[name = tensor<string, []>("x_405_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_405_transpose_y_0 = const()[name = tensor<string, []>("x_405_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_37 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3412)[name = tensor<string, []>("transpose_185")];
            tensor<fp32, [1, 8, 188, 375]> x_405 = matmul(transpose_x = x_405_transpose_x_0, transpose_y = x_405_transpose_y_0, x = q_with_bias_v_37, y = op_3414_quantized)[name = tensor<string, []>("x_405")];
            tensor<fp32, []> const_194 = const()[name = tensor<string, []>("const_194"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_407_pad_0 = const()[name = tensor<string, []>("x_407_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_407_mode_0 = const()[name = tensor<string, []>("x_407_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_407 = pad(constant_val = const_194, mode = x_407_mode_0, pad = x_407_pad_0, x = x_405)[name = tensor<string, []>("x_407")];
            tensor<int32, [4]> var_3422 = const()[name = tensor<string, []>("op_3422"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_409 = reshape(shape = var_3422, x = x_407)[name = tensor<string, []>("x_409")];
            tensor<int32, [4]> var_3426_begin_0 = const()[name = tensor<string, []>("op_3426_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3426_end_0 = const()[name = tensor<string, []>("op_3426_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3426_end_mask_0 = const()[name = tensor<string, []>("op_3426_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_3426 = slice_by_index(begin = var_3426_begin_0, end = var_3426_end_0, end_mask = var_3426_end_mask_0, x = x_409)[name = tensor<string, []>("op_3426")];
            tensor<int32, [4]> var_3427 = const()[name = tensor<string, []>("op_3427"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_73 = reshape(shape = var_3427, x = var_3426)[name = tensor<string, []>("matrix_bd_73")];
            tensor<bool, []> matrix_ac_37_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_37_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_132_perm_0 = const()[name = tensor<string, []>("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_133_perm_0 = const()[name = tensor<string, []>("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73)[name = tensor<string, []>("transpose_183")];
            tensor<fp32, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3410)[name = tensor<string, []>("transpose_184")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_37 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = tensor<string, []>("matrix_ac_37")];
            tensor<int32, [4]> matrix_bd_75_begin_0 = const()[name = tensor<string, []>("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = tensor<string, []>("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_75 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73)[name = tensor<string, []>("matrix_bd_75")];
            tensor<fp32, [1, 8, 188, 188]> var_3436 = add(x = matrix_ac_37, y = matrix_bd_75)[name = tensor<string, []>("op_3436")];
            tensor<fp32, []> _inversed_scores_73_y_0 = const()[name = tensor<string, []>("_inversed_scores_73_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_73 = mul(x = var_3436, y = _inversed_scores_73_y_0)[name = tensor<string, []>("_inversed_scores_73")];
            tensor<fp32, [1, 8, 188, 188]> scores_75 = select(a = var_14, b = _inversed_scores_73, cond = mask_3)[name = tensor<string, []>("scores_75")];
            tensor<fp32, [1, 8, 188, 188]> var_3442 = softmax(axis = var_32, x = scores_75)[name = tensor<string, []>("op_3442")];
            tensor<fp32, [1, 8, 188, 188]> input_969 = select(a = var_13, b = var_3442, cond = mask_3)[name = tensor<string, []>("input_969")];
            tensor<bool, []> x_411_transpose_x_0 = const()[name = tensor<string, []>("x_411_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_411_transpose_y_0 = const()[name = tensor<string, []>("x_411_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_39 = transpose(perm = value_39_perm_0, x = v_37)[name = tensor<string, []>("transpose_182")];
            tensor<fp32, [1, 8, 188, 128]> x_411 = matmul(transpose_x = x_411_transpose_x_0, transpose_y = x_411_transpose_y_0, x = input_969, y = value_39)[name = tensor<string, []>("x_411")];
            tensor<int32, [4]> var_3446_perm_0 = const()[name = tensor<string, []>("op_3446_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3447 = const()[name = tensor<string, []>("op_3447"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_3446 = transpose(perm = var_3446_perm_0, x = x_411)[name = tensor<string, []>("transpose_181")];
            tensor<fp32, [1, 188, 1024]> input_971 = reshape(shape = var_3447, x = var_3446)[name = tensor<string, []>("input_971")];
            tensor<fp32, [1, 188, 1024]> input_973 = linear(bias = encoder_layers_18_self_attn_linear_out_bias, weight = encoder_layers_18_self_attn_linear_out_weight_quantized, x = input_971)[name = tensor<string, []>("linear_169")];
            tensor<fp32, [1, 188, 1024]> input_975 = add(x = input_967, y = input_973)[name = tensor<string, []>("input_975")];
            tensor<int32, [1]> x_415_axes_0 = const()[name = tensor<string, []>("x_415_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_415 = layer_norm(axes = x_415_axes_0, beta = encoder_layers_18_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_18_norm_conv_weight, x = input_975)[name = tensor<string, []>("x_415")];
            tensor<int32, [3]> input_977_perm_0 = const()[name = tensor<string, []>("input_977_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_979_pad_type_0 = const()[name = tensor<string, []>("input_979_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_979_strides_0 = const()[name = tensor<string, []>("input_979_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_979_pad_0 = const()[name = tensor<string, []>("input_979_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_979_dilations_0 = const()[name = tensor<string, []>("input_979_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_979_groups_0 = const()[name = tensor<string, []>("input_979_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_977 = transpose(perm = input_977_perm_0, x = x_415)[name = tensor<string, []>("transpose_180")];
            tensor<fp32, [1, 2048, 188]> input_979 = conv(bias = encoder_layers_18_conv_pointwise_conv1_bias, dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = encoder_layers_18_conv_pointwise_conv1_weight_quantized, x = input_977)[name = tensor<string, []>("input_979")];
            tensor<int32, []> x_417_split_num_splits_0 = const()[name = tensor<string, []>("x_417_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_417_split_axis_0 = const()[name = tensor<string, []>("x_417_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_417_split_0, tensor<fp32, [1, 1024, 188]> x_417_split_1 = split(axis = x_417_split_axis_0, num_splits = x_417_split_num_splits_0, x = input_979)[name = tensor<string, []>("x_417_split")];
            tensor<fp32, [1, 1024, 188]> x_417_split_1_sigmoid = sigmoid(x = x_417_split_1)[name = tensor<string, []>("x_417_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_417 = mul(x = x_417_split_0, y = x_417_split_1_sigmoid)[name = tensor<string, []>("x_417")];
            tensor<fp32, [1, 1024, 188]> input_981 = select(a = var_13, b = x_417, cond = var_339)[name = tensor<string, []>("input_981")];
            tensor<fp32, []> const_197 = const()[name = tensor<string, []>("const_197"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_983_pad_0 = const()[name = tensor<string, []>("input_983_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_983_mode_0 = const()[name = tensor<string, []>("input_983_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_983 = pad(constant_val = const_197, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981)[name = tensor<string, []>("input_983")];
            tensor<string, []> input_985_pad_type_0 = const()[name = tensor<string, []>("input_985_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_985_groups_0 = const()[name = tensor<string, []>("input_985_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_985_strides_0 = const()[name = tensor<string, []>("input_985_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_985_pad_0 = const()[name = tensor<string, []>("input_985_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_985_dilations_0 = const()[name = tensor<string, []>("input_985_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_284_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_284_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595347136))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595357504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595356416)))];
            tensor<fp32, [1024]> const_285 = const()[name = tensor<string, []>("const_285"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595361664)))];
            tensor<fp32, [1, 1024, 188]> input_987 = conv(bias = const_285, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_quantized, x = input_983)[name = tensor<string, []>("input_987")];
            tensor<fp32, [1, 1024, 188]> input_989 = silu(x = input_987)[name = tensor<string, []>("input_989")];
            tensor<string, []> x_419_pad_type_0 = const()[name = tensor<string, []>("x_419_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_419_strides_0 = const()[name = tensor<string, []>("x_419_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_419_pad_0 = const()[name = tensor<string, []>("x_419_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_419_dilations_0 = const()[name = tensor<string, []>("x_419_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_419_groups_0 = const()[name = tensor<string, []>("x_419_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_419 = conv(bias = encoder_layers_18_conv_pointwise_conv2_bias, dilations = x_419_dilations_0, groups = x_419_groups_0, pad = x_419_pad_0, pad_type = x_419_pad_type_0, strides = x_419_strides_0, weight = encoder_layers_18_conv_pointwise_conv2_weight_quantized, x = input_989)[name = tensor<string, []>("x_419")];
            tensor<int32, [3]> input_991_perm_0 = const()[name = tensor<string, []>("input_991_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_991 = transpose(perm = input_991_perm_0, x = x_419)[name = tensor<string, []>("transpose_179")];
            tensor<fp32, [1, 188, 1024]> input_993 = add(x = input_975, y = input_991)[name = tensor<string, []>("input_993")];
            tensor<int32, [1]> input_995_axes_0 = const()[name = tensor<string, []>("input_995_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_995 = layer_norm(axes = input_995_axes_0, beta = encoder_layers_18_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_18_norm_feed_forward2_weight, x = input_993)[name = tensor<string, []>("input_995")];
            tensor<fp32, [1, 188, 4096]> input_997 = linear(bias = encoder_layers_18_feed_forward2_linear1_bias, weight = encoder_layers_18_feed_forward2_linear1_weight_quantized, x = input_995)[name = tensor<string, []>("linear_170")];
            tensor<fp32, [1, 188, 4096]> input_999 = silu(x = input_997)[name = tensor<string, []>("input_999")];
            tensor<fp32, [1, 188, 1024]> input_1003 = linear(bias = encoder_layers_18_feed_forward2_linear2_bias, weight = encoder_layers_18_feed_forward2_linear2_weight_quantized, x = input_999)[name = tensor<string, []>("linear_171")];
            tensor<fp32, []> var_3513 = const()[name = tensor<string, []>("op_3513"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3514 = mul(x = input_1003, y = var_3513)[name = tensor<string, []>("op_3514")];
            tensor<fp32, [1, 188, 1024]> input_1005 = add(x = input_993, y = var_3514)[name = tensor<string, []>("input_1005")];
            tensor<int32, [1]> input_1007_axes_0 = const()[name = tensor<string, []>("input_1007_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1007 = layer_norm(axes = input_1007_axes_0, beta = encoder_layers_18_norm_out_bias, epsilon = var_11, gamma = encoder_layers_18_norm_out_weight, x = input_1005)[name = tensor<string, []>("input_1007")];
            tensor<int32, [1]> input_1009_axes_0 = const()[name = tensor<string, []>("input_1009_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1009 = layer_norm(axes = input_1009_axes_0, beta = encoder_layers_19_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_19_norm_feed_forward1_weight, x = input_1007)[name = tensor<string, []>("input_1009")];
            tensor<fp32, [1, 188, 4096]> input_1011 = linear(bias = encoder_layers_19_feed_forward1_linear1_bias, weight = encoder_layers_19_feed_forward1_linear1_weight_quantized, x = input_1009)[name = tensor<string, []>("linear_172")];
            tensor<fp32, [1, 188, 4096]> input_1013 = silu(x = input_1011)[name = tensor<string, []>("input_1013")];
            tensor<fp32, [1, 188, 1024]> input_1017 = linear(bias = encoder_layers_19_feed_forward1_linear2_bias, weight = encoder_layers_19_feed_forward1_linear2_weight_quantized, x = input_1013)[name = tensor<string, []>("linear_173")];
            tensor<fp32, []> var_3544 = const()[name = tensor<string, []>("op_3544"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3545 = mul(x = input_1017, y = var_3544)[name = tensor<string, []>("op_3545")];
            tensor<fp32, [1, 188, 1024]> input_1019 = add(x = input_1007, y = var_3545)[name = tensor<string, []>("input_1019")];
            tensor<int32, [1]> query_39_axes_0 = const()[name = tensor<string, []>("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_39 = layer_norm(axes = query_39_axes_0, beta = encoder_layers_19_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_19_norm_self_att_weight, x = input_1019)[name = tensor<string, []>("query_39")];
            tensor<fp32, [1, 188, 1024]> var_3561 = linear(bias = encoder_layers_19_self_attn_linear_q_bias, weight = encoder_layers_19_self_attn_linear_q_weight_quantized, x = query_39)[name = tensor<string, []>("linear_174")];
            tensor<int32, [4]> var_3562 = const()[name = tensor<string, []>("op_3562"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_115 = reshape(shape = var_3562, x = var_3561)[name = tensor<string, []>("q_115")];
            tensor<fp32, [1, 188, 1024]> var_3566 = linear(bias = encoder_layers_19_self_attn_linear_k_bias, weight = encoder_layers_19_self_attn_linear_k_weight_quantized, x = query_39)[name = tensor<string, []>("linear_175")];
            tensor<int32, [4]> var_3567 = const()[name = tensor<string, []>("op_3567"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_77 = reshape(shape = var_3567, x = var_3566)[name = tensor<string, []>("k_77")];
            tensor<fp32, [1, 188, 1024]> var_3571 = linear(bias = encoder_layers_19_self_attn_linear_v_bias, weight = encoder_layers_19_self_attn_linear_v_weight_quantized, x = query_39)[name = tensor<string, []>("linear_176")];
            tensor<int32, [4]> var_3572 = const()[name = tensor<string, []>("op_3572"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_39 = reshape(shape = var_3572, x = var_3571)[name = tensor<string, []>("v_39")];
            tensor<int32, [4]> value_41_perm_0 = const()[name = tensor<string, []>("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_3584 = add(x = q_115, y = encoder_layers_19_self_attn_pos_bias_u)[name = tensor<string, []>("op_3584")];
            tensor<fp32, [1, 188, 8, 128]> var_3586 = add(x = q_115, y = encoder_layers_19_self_attn_pos_bias_v)[name = tensor<string, []>("op_3586")];
            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_3588_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3588_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595365824))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595750336))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595749888)))];
            tensor<bool, []> x_427_transpose_x_0 = const()[name = tensor<string, []>("x_427_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_427_transpose_y_0 = const()[name = tensor<string, []>("x_427_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_39 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3586)[name = tensor<string, []>("transpose_178")];
            tensor<fp32, [1, 8, 188, 375]> x_427 = matmul(transpose_x = x_427_transpose_x_0, transpose_y = x_427_transpose_y_0, x = q_with_bias_v_39, y = op_3588_quantized)[name = tensor<string, []>("x_427")];
            tensor<fp32, []> const_204 = const()[name = tensor<string, []>("const_204"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_429_pad_0 = const()[name = tensor<string, []>("x_429_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_429_mode_0 = const()[name = tensor<string, []>("x_429_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_429 = pad(constant_val = const_204, mode = x_429_mode_0, pad = x_429_pad_0, x = x_427)[name = tensor<string, []>("x_429")];
            tensor<int32, [4]> var_3596 = const()[name = tensor<string, []>("op_3596"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_431 = reshape(shape = var_3596, x = x_429)[name = tensor<string, []>("x_431")];
            tensor<int32, [4]> var_3600_begin_0 = const()[name = tensor<string, []>("op_3600_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3600_end_0 = const()[name = tensor<string, []>("op_3600_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3600_end_mask_0 = const()[name = tensor<string, []>("op_3600_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_3600 = slice_by_index(begin = var_3600_begin_0, end = var_3600_end_0, end_mask = var_3600_end_mask_0, x = x_431)[name = tensor<string, []>("op_3600")];
            tensor<int32, [4]> var_3601 = const()[name = tensor<string, []>("op_3601"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_77 = reshape(shape = var_3601, x = var_3600)[name = tensor<string, []>("matrix_bd_77")];
            tensor<bool, []> matrix_ac_39_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_39_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_39_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_39_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_134_perm_0 = const()[name = tensor<string, []>("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_135_perm_0 = const()[name = tensor<string, []>("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77)[name = tensor<string, []>("transpose_176")];
            tensor<fp32, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3584)[name = tensor<string, []>("transpose_177")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_39 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = tensor<string, []>("matrix_ac_39")];
            tensor<int32, [4]> matrix_bd_79_begin_0 = const()[name = tensor<string, []>("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = tensor<string, []>("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_79 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77)[name = tensor<string, []>("matrix_bd_79")];
            tensor<fp32, [1, 8, 188, 188]> var_3610 = add(x = matrix_ac_39, y = matrix_bd_79)[name = tensor<string, []>("op_3610")];
            tensor<fp32, []> _inversed_scores_77_y_0 = const()[name = tensor<string, []>("_inversed_scores_77_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_77 = mul(x = var_3610, y = _inversed_scores_77_y_0)[name = tensor<string, []>("_inversed_scores_77")];
            tensor<fp32, [1, 8, 188, 188]> scores_79 = select(a = var_14, b = _inversed_scores_77, cond = mask_3)[name = tensor<string, []>("scores_79")];
            tensor<fp32, [1, 8, 188, 188]> var_3616 = softmax(axis = var_32, x = scores_79)[name = tensor<string, []>("op_3616")];
            tensor<fp32, [1, 8, 188, 188]> input_1021 = select(a = var_13, b = var_3616, cond = mask_3)[name = tensor<string, []>("input_1021")];
            tensor<bool, []> x_433_transpose_x_0 = const()[name = tensor<string, []>("x_433_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_433_transpose_y_0 = const()[name = tensor<string, []>("x_433_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_41 = transpose(perm = value_41_perm_0, x = v_39)[name = tensor<string, []>("transpose_175")];
            tensor<fp32, [1, 8, 188, 128]> x_433 = matmul(transpose_x = x_433_transpose_x_0, transpose_y = x_433_transpose_y_0, x = input_1021, y = value_41)[name = tensor<string, []>("x_433")];
            tensor<int32, [4]> var_3620_perm_0 = const()[name = tensor<string, []>("op_3620_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3621 = const()[name = tensor<string, []>("op_3621"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_3620 = transpose(perm = var_3620_perm_0, x = x_433)[name = tensor<string, []>("transpose_174")];
            tensor<fp32, [1, 188, 1024]> input_1023 = reshape(shape = var_3621, x = var_3620)[name = tensor<string, []>("input_1023")];
            tensor<fp32, [1, 188, 1024]> input_1025 = linear(bias = encoder_layers_19_self_attn_linear_out_bias, weight = encoder_layers_19_self_attn_linear_out_weight_quantized, x = input_1023)[name = tensor<string, []>("linear_178")];
            tensor<fp32, [1, 188, 1024]> input_1027 = add(x = input_1019, y = input_1025)[name = tensor<string, []>("input_1027")];
            tensor<int32, [1]> x_437_axes_0 = const()[name = tensor<string, []>("x_437_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_437 = layer_norm(axes = x_437_axes_0, beta = encoder_layers_19_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_19_norm_conv_weight, x = input_1027)[name = tensor<string, []>("x_437")];
            tensor<int32, [3]> input_1029_perm_0 = const()[name = tensor<string, []>("input_1029_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1031_pad_type_0 = const()[name = tensor<string, []>("input_1031_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1031_strides_0 = const()[name = tensor<string, []>("input_1031_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1031_pad_0 = const()[name = tensor<string, []>("input_1031_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1031_dilations_0 = const()[name = tensor<string, []>("input_1031_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1031_groups_0 = const()[name = tensor<string, []>("input_1031_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_1029 = transpose(perm = input_1029_perm_0, x = x_437)[name = tensor<string, []>("transpose_173")];
            tensor<fp32, [1, 2048, 188]> input_1031 = conv(bias = encoder_layers_19_conv_pointwise_conv1_bias, dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = encoder_layers_19_conv_pointwise_conv1_weight_quantized, x = input_1029)[name = tensor<string, []>("input_1031")];
            tensor<int32, []> x_439_split_num_splits_0 = const()[name = tensor<string, []>("x_439_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_439_split_axis_0 = const()[name = tensor<string, []>("x_439_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_439_split_0, tensor<fp32, [1, 1024, 188]> x_439_split_1 = split(axis = x_439_split_axis_0, num_splits = x_439_split_num_splits_0, x = input_1031)[name = tensor<string, []>("x_439_split")];
            tensor<fp32, [1, 1024, 188]> x_439_split_1_sigmoid = sigmoid(x = x_439_split_1)[name = tensor<string, []>("x_439_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_439 = mul(x = x_439_split_0, y = x_439_split_1_sigmoid)[name = tensor<string, []>("x_439")];
            tensor<fp32, [1, 1024, 188]> input_1033 = select(a = var_13, b = x_439, cond = var_339)[name = tensor<string, []>("input_1033")];
            tensor<fp32, []> const_207 = const()[name = tensor<string, []>("const_207"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_1035_pad_0 = const()[name = tensor<string, []>("input_1035_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1035_mode_0 = const()[name = tensor<string, []>("input_1035_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_1035 = pad(constant_val = const_207, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033)[name = tensor<string, []>("input_1035")];
            tensor<string, []> input_1037_pad_type_0 = const()[name = tensor<string, []>("input_1037_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1037_groups_0 = const()[name = tensor<string, []>("input_1037_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1037_strides_0 = const()[name = tensor<string, []>("input_1037_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1037_pad_0 = const()[name = tensor<string, []>("input_1037_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1037_dilations_0 = const()[name = tensor<string, []>("input_1037_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_286_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_286_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595751936))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595762304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595761216)))];
            tensor<fp32, [1024]> const_287 = const()[name = tensor<string, []>("const_287"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595766464)))];
            tensor<fp32, [1, 1024, 188]> input_1039 = conv(bias = const_287, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_quantized, x = input_1035)[name = tensor<string, []>("input_1039")];
            tensor<fp32, [1, 1024, 188]> input_1041 = silu(x = input_1039)[name = tensor<string, []>("input_1041")];
            tensor<string, []> x_441_pad_type_0 = const()[name = tensor<string, []>("x_441_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_441_strides_0 = const()[name = tensor<string, []>("x_441_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_441_pad_0 = const()[name = tensor<string, []>("x_441_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_441_dilations_0 = const()[name = tensor<string, []>("x_441_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_441_groups_0 = const()[name = tensor<string, []>("x_441_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_441 = conv(bias = encoder_layers_19_conv_pointwise_conv2_bias, dilations = x_441_dilations_0, groups = x_441_groups_0, pad = x_441_pad_0, pad_type = x_441_pad_type_0, strides = x_441_strides_0, weight = encoder_layers_19_conv_pointwise_conv2_weight_quantized, x = input_1041)[name = tensor<string, []>("x_441")];
            tensor<int32, [3]> input_1043_perm_0 = const()[name = tensor<string, []>("input_1043_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_1043 = transpose(perm = input_1043_perm_0, x = x_441)[name = tensor<string, []>("transpose_172")];
            tensor<fp32, [1, 188, 1024]> input_1045 = add(x = input_1027, y = input_1043)[name = tensor<string, []>("input_1045")];
            tensor<int32, [1]> input_1047_axes_0 = const()[name = tensor<string, []>("input_1047_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1047 = layer_norm(axes = input_1047_axes_0, beta = encoder_layers_19_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_19_norm_feed_forward2_weight, x = input_1045)[name = tensor<string, []>("input_1047")];
            tensor<fp32, [1, 188, 4096]> input_1049 = linear(bias = encoder_layers_19_feed_forward2_linear1_bias, weight = encoder_layers_19_feed_forward2_linear1_weight_quantized, x = input_1047)[name = tensor<string, []>("linear_179")];
            tensor<fp32, [1, 188, 4096]> input_1051 = silu(x = input_1049)[name = tensor<string, []>("input_1051")];
            tensor<fp32, [1, 188, 1024]> input_1055 = linear(bias = encoder_layers_19_feed_forward2_linear2_bias, weight = encoder_layers_19_feed_forward2_linear2_weight_quantized, x = input_1051)[name = tensor<string, []>("linear_180")];
            tensor<fp32, []> var_3687 = const()[name = tensor<string, []>("op_3687"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3688 = mul(x = input_1055, y = var_3687)[name = tensor<string, []>("op_3688")];
            tensor<fp32, [1, 188, 1024]> input_1057 = add(x = input_1045, y = var_3688)[name = tensor<string, []>("input_1057")];
            tensor<int32, [1]> input_1059_axes_0 = const()[name = tensor<string, []>("input_1059_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1059 = layer_norm(axes = input_1059_axes_0, beta = encoder_layers_19_norm_out_bias, epsilon = var_11, gamma = encoder_layers_19_norm_out_weight, x = input_1057)[name = tensor<string, []>("input_1059")];
            tensor<int32, [1]> input_1061_axes_0 = const()[name = tensor<string, []>("input_1061_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1061 = layer_norm(axes = input_1061_axes_0, beta = encoder_layers_20_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_20_norm_feed_forward1_weight, x = input_1059)[name = tensor<string, []>("input_1061")];
            tensor<fp32, [1, 188, 4096]> input_1063 = linear(bias = encoder_layers_20_feed_forward1_linear1_bias, weight = encoder_layers_20_feed_forward1_linear1_weight_quantized, x = input_1061)[name = tensor<string, []>("linear_181")];
            tensor<fp32, [1, 188, 4096]> input_1065 = silu(x = input_1063)[name = tensor<string, []>("input_1065")];
            tensor<fp32, [1, 188, 1024]> input_1069 = linear(bias = encoder_layers_20_feed_forward1_linear2_bias, weight = encoder_layers_20_feed_forward1_linear2_weight_quantized, x = input_1065)[name = tensor<string, []>("linear_182")];
            tensor<fp32, []> var_3718 = const()[name = tensor<string, []>("op_3718"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3719 = mul(x = input_1069, y = var_3718)[name = tensor<string, []>("op_3719")];
            tensor<fp32, [1, 188, 1024]> input_1071 = add(x = input_1059, y = var_3719)[name = tensor<string, []>("input_1071")];
            tensor<int32, [1]> query_41_axes_0 = const()[name = tensor<string, []>("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_41 = layer_norm(axes = query_41_axes_0, beta = encoder_layers_20_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_20_norm_self_att_weight, x = input_1071)[name = tensor<string, []>("query_41")];
            tensor<fp32, [1, 188, 1024]> var_3735 = linear(bias = encoder_layers_20_self_attn_linear_q_bias, weight = encoder_layers_20_self_attn_linear_q_weight_quantized, x = query_41)[name = tensor<string, []>("linear_183")];
            tensor<int32, [4]> var_3736 = const()[name = tensor<string, []>("op_3736"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_121 = reshape(shape = var_3736, x = var_3735)[name = tensor<string, []>("q_121")];
            tensor<fp32, [1, 188, 1024]> var_3740 = linear(bias = encoder_layers_20_self_attn_linear_k_bias, weight = encoder_layers_20_self_attn_linear_k_weight_quantized, x = query_41)[name = tensor<string, []>("linear_184")];
            tensor<int32, [4]> var_3741 = const()[name = tensor<string, []>("op_3741"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_81 = reshape(shape = var_3741, x = var_3740)[name = tensor<string, []>("k_81")];
            tensor<fp32, [1, 188, 1024]> var_3745 = linear(bias = encoder_layers_20_self_attn_linear_v_bias, weight = encoder_layers_20_self_attn_linear_v_weight_quantized, x = query_41)[name = tensor<string, []>("linear_185")];
            tensor<int32, [4]> var_3746 = const()[name = tensor<string, []>("op_3746"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_41 = reshape(shape = var_3746, x = var_3745)[name = tensor<string, []>("v_41")];
            tensor<int32, [4]> value_43_perm_0 = const()[name = tensor<string, []>("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_3758 = add(x = q_121, y = encoder_layers_20_self_attn_pos_bias_u)[name = tensor<string, []>("op_3758")];
            tensor<fp32, [1, 188, 8, 128]> var_3760 = add(x = q_121, y = encoder_layers_20_self_attn_pos_bias_v)[name = tensor<string, []>("op_3760")];
            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_3762_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3762_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595770624))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596155136))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596154688)))];
            tensor<bool, []> x_449_transpose_x_0 = const()[name = tensor<string, []>("x_449_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_449_transpose_y_0 = const()[name = tensor<string, []>("x_449_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_41 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3760)[name = tensor<string, []>("transpose_171")];
            tensor<fp32, [1, 8, 188, 375]> x_449 = matmul(transpose_x = x_449_transpose_x_0, transpose_y = x_449_transpose_y_0, x = q_with_bias_v_41, y = op_3762_quantized)[name = tensor<string, []>("x_449")];
            tensor<fp32, []> const_214 = const()[name = tensor<string, []>("const_214"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_451_pad_0 = const()[name = tensor<string, []>("x_451_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_451_mode_0 = const()[name = tensor<string, []>("x_451_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_451 = pad(constant_val = const_214, mode = x_451_mode_0, pad = x_451_pad_0, x = x_449)[name = tensor<string, []>("x_451")];
            tensor<int32, [4]> var_3770 = const()[name = tensor<string, []>("op_3770"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_453 = reshape(shape = var_3770, x = x_451)[name = tensor<string, []>("x_453")];
            tensor<int32, [4]> var_3774_begin_0 = const()[name = tensor<string, []>("op_3774_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3774_end_0 = const()[name = tensor<string, []>("op_3774_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3774_end_mask_0 = const()[name = tensor<string, []>("op_3774_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_3774 = slice_by_index(begin = var_3774_begin_0, end = var_3774_end_0, end_mask = var_3774_end_mask_0, x = x_453)[name = tensor<string, []>("op_3774")];
            tensor<int32, [4]> var_3775 = const()[name = tensor<string, []>("op_3775"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_81 = reshape(shape = var_3775, x = var_3774)[name = tensor<string, []>("matrix_bd_81")];
            tensor<bool, []> matrix_ac_41_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_41_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_41_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_41_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_136_perm_0 = const()[name = tensor<string, []>("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_137_perm_0 = const()[name = tensor<string, []>("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81)[name = tensor<string, []>("transpose_169")];
            tensor<fp32, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3758)[name = tensor<string, []>("transpose_170")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_41 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = tensor<string, []>("matrix_ac_41")];
            tensor<int32, [4]> matrix_bd_83_begin_0 = const()[name = tensor<string, []>("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = tensor<string, []>("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_83 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81)[name = tensor<string, []>("matrix_bd_83")];
            tensor<fp32, [1, 8, 188, 188]> var_3784 = add(x = matrix_ac_41, y = matrix_bd_83)[name = tensor<string, []>("op_3784")];
            tensor<fp32, []> _inversed_scores_81_y_0 = const()[name = tensor<string, []>("_inversed_scores_81_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_81 = mul(x = var_3784, y = _inversed_scores_81_y_0)[name = tensor<string, []>("_inversed_scores_81")];
            tensor<fp32, [1, 8, 188, 188]> scores_83 = select(a = var_14, b = _inversed_scores_81, cond = mask_3)[name = tensor<string, []>("scores_83")];
            tensor<fp32, [1, 8, 188, 188]> var_3790 = softmax(axis = var_32, x = scores_83)[name = tensor<string, []>("op_3790")];
            tensor<fp32, [1, 8, 188, 188]> input_1073 = select(a = var_13, b = var_3790, cond = mask_3)[name = tensor<string, []>("input_1073")];
            tensor<bool, []> x_455_transpose_x_0 = const()[name = tensor<string, []>("x_455_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_455_transpose_y_0 = const()[name = tensor<string, []>("x_455_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_43 = transpose(perm = value_43_perm_0, x = v_41)[name = tensor<string, []>("transpose_168")];
            tensor<fp32, [1, 8, 188, 128]> x_455 = matmul(transpose_x = x_455_transpose_x_0, transpose_y = x_455_transpose_y_0, x = input_1073, y = value_43)[name = tensor<string, []>("x_455")];
            tensor<int32, [4]> var_3794_perm_0 = const()[name = tensor<string, []>("op_3794_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3795 = const()[name = tensor<string, []>("op_3795"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_3794 = transpose(perm = var_3794_perm_0, x = x_455)[name = tensor<string, []>("transpose_167")];
            tensor<fp32, [1, 188, 1024]> input_1075 = reshape(shape = var_3795, x = var_3794)[name = tensor<string, []>("input_1075")];
            tensor<fp32, [1, 188, 1024]> input_1077 = linear(bias = encoder_layers_20_self_attn_linear_out_bias, weight = encoder_layers_20_self_attn_linear_out_weight_quantized, x = input_1075)[name = tensor<string, []>("linear_187")];
            tensor<fp32, [1, 188, 1024]> input_1079 = add(x = input_1071, y = input_1077)[name = tensor<string, []>("input_1079")];
            tensor<int32, [1]> x_459_axes_0 = const()[name = tensor<string, []>("x_459_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_459 = layer_norm(axes = x_459_axes_0, beta = encoder_layers_20_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_20_norm_conv_weight, x = input_1079)[name = tensor<string, []>("x_459")];
            tensor<int32, [3]> input_1081_perm_0 = const()[name = tensor<string, []>("input_1081_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1083_pad_type_0 = const()[name = tensor<string, []>("input_1083_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1083_strides_0 = const()[name = tensor<string, []>("input_1083_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1083_pad_0 = const()[name = tensor<string, []>("input_1083_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1083_dilations_0 = const()[name = tensor<string, []>("input_1083_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1083_groups_0 = const()[name = tensor<string, []>("input_1083_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_1081 = transpose(perm = input_1081_perm_0, x = x_459)[name = tensor<string, []>("transpose_166")];
            tensor<fp32, [1, 2048, 188]> input_1083 = conv(bias = encoder_layers_20_conv_pointwise_conv1_bias, dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = encoder_layers_20_conv_pointwise_conv1_weight_quantized, x = input_1081)[name = tensor<string, []>("input_1083")];
            tensor<int32, []> x_461_split_num_splits_0 = const()[name = tensor<string, []>("x_461_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_461_split_axis_0 = const()[name = tensor<string, []>("x_461_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_461_split_0, tensor<fp32, [1, 1024, 188]> x_461_split_1 = split(axis = x_461_split_axis_0, num_splits = x_461_split_num_splits_0, x = input_1083)[name = tensor<string, []>("x_461_split")];
            tensor<fp32, [1, 1024, 188]> x_461_split_1_sigmoid = sigmoid(x = x_461_split_1)[name = tensor<string, []>("x_461_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_461 = mul(x = x_461_split_0, y = x_461_split_1_sigmoid)[name = tensor<string, []>("x_461")];
            tensor<fp32, [1, 1024, 188]> input_1085 = select(a = var_13, b = x_461, cond = var_339)[name = tensor<string, []>("input_1085")];
            tensor<fp32, []> const_217 = const()[name = tensor<string, []>("const_217"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_1087_pad_0 = const()[name = tensor<string, []>("input_1087_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1087_mode_0 = const()[name = tensor<string, []>("input_1087_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_1087 = pad(constant_val = const_217, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085)[name = tensor<string, []>("input_1087")];
            tensor<string, []> input_1089_pad_type_0 = const()[name = tensor<string, []>("input_1089_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1089_groups_0 = const()[name = tensor<string, []>("input_1089_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1089_strides_0 = const()[name = tensor<string, []>("input_1089_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1089_pad_0 = const()[name = tensor<string, []>("input_1089_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1089_dilations_0 = const()[name = tensor<string, []>("input_1089_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_288_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_288_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596156736))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596167104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596166016)))];
            tensor<fp32, [1024]> const_289 = const()[name = tensor<string, []>("const_289"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596171264)))];
            tensor<fp32, [1, 1024, 188]> input_1091 = conv(bias = const_289, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_quantized, x = input_1087)[name = tensor<string, []>("input_1091")];
            tensor<fp32, [1, 1024, 188]> input_1093 = silu(x = input_1091)[name = tensor<string, []>("input_1093")];
            tensor<string, []> x_463_pad_type_0 = const()[name = tensor<string, []>("x_463_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_463_strides_0 = const()[name = tensor<string, []>("x_463_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_463_pad_0 = const()[name = tensor<string, []>("x_463_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_463_dilations_0 = const()[name = tensor<string, []>("x_463_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_463_groups_0 = const()[name = tensor<string, []>("x_463_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_463 = conv(bias = encoder_layers_20_conv_pointwise_conv2_bias, dilations = x_463_dilations_0, groups = x_463_groups_0, pad = x_463_pad_0, pad_type = x_463_pad_type_0, strides = x_463_strides_0, weight = encoder_layers_20_conv_pointwise_conv2_weight_quantized, x = input_1093)[name = tensor<string, []>("x_463")];
            tensor<int32, [3]> input_1095_perm_0 = const()[name = tensor<string, []>("input_1095_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_1095 = transpose(perm = input_1095_perm_0, x = x_463)[name = tensor<string, []>("transpose_165")];
            tensor<fp32, [1, 188, 1024]> input_1097 = add(x = input_1079, y = input_1095)[name = tensor<string, []>("input_1097")];
            tensor<int32, [1]> input_1099_axes_0 = const()[name = tensor<string, []>("input_1099_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1099 = layer_norm(axes = input_1099_axes_0, beta = encoder_layers_20_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_20_norm_feed_forward2_weight, x = input_1097)[name = tensor<string, []>("input_1099")];
            tensor<fp32, [1, 188, 4096]> input_1101 = linear(bias = encoder_layers_20_feed_forward2_linear1_bias, weight = encoder_layers_20_feed_forward2_linear1_weight_quantized, x = input_1099)[name = tensor<string, []>("linear_188")];
            tensor<fp32, [1, 188, 4096]> input_1103 = silu(x = input_1101)[name = tensor<string, []>("input_1103")];
            tensor<fp32, [1, 188, 1024]> input_1107 = linear(bias = encoder_layers_20_feed_forward2_linear2_bias, weight = encoder_layers_20_feed_forward2_linear2_weight_quantized, x = input_1103)[name = tensor<string, []>("linear_189")];
            tensor<fp32, []> var_3861 = const()[name = tensor<string, []>("op_3861"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3862 = mul(x = input_1107, y = var_3861)[name = tensor<string, []>("op_3862")];
            tensor<fp32, [1, 188, 1024]> input_1109 = add(x = input_1097, y = var_3862)[name = tensor<string, []>("input_1109")];
            tensor<int32, [1]> input_1111_axes_0 = const()[name = tensor<string, []>("input_1111_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1111 = layer_norm(axes = input_1111_axes_0, beta = encoder_layers_20_norm_out_bias, epsilon = var_11, gamma = encoder_layers_20_norm_out_weight, x = input_1109)[name = tensor<string, []>("input_1111")];
            tensor<int32, [1]> input_1113_axes_0 = const()[name = tensor<string, []>("input_1113_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1113 = layer_norm(axes = input_1113_axes_0, beta = encoder_layers_21_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_21_norm_feed_forward1_weight, x = input_1111)[name = tensor<string, []>("input_1113")];
            tensor<fp32, [1, 188, 4096]> input_1115 = linear(bias = encoder_layers_21_feed_forward1_linear1_bias, weight = encoder_layers_21_feed_forward1_linear1_weight_quantized, x = input_1113)[name = tensor<string, []>("linear_190")];
            tensor<fp32, [1, 188, 4096]> input_1117 = silu(x = input_1115)[name = tensor<string, []>("input_1117")];
            tensor<fp32, [1, 188, 1024]> input_1121 = linear(bias = encoder_layers_21_feed_forward1_linear2_bias, weight = encoder_layers_21_feed_forward1_linear2_weight_quantized, x = input_1117)[name = tensor<string, []>("linear_191")];
            tensor<fp32, []> var_3892 = const()[name = tensor<string, []>("op_3892"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_3893 = mul(x = input_1121, y = var_3892)[name = tensor<string, []>("op_3893")];
            tensor<fp32, [1, 188, 1024]> input_1123 = add(x = input_1111, y = var_3893)[name = tensor<string, []>("input_1123")];
            tensor<int32, [1]> query_43_axes_0 = const()[name = tensor<string, []>("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_43 = layer_norm(axes = query_43_axes_0, beta = encoder_layers_21_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_21_norm_self_att_weight, x = input_1123)[name = tensor<string, []>("query_43")];
            tensor<fp32, [1, 188, 1024]> var_3909 = linear(bias = encoder_layers_21_self_attn_linear_q_bias, weight = encoder_layers_21_self_attn_linear_q_weight_quantized, x = query_43)[name = tensor<string, []>("linear_192")];
            tensor<int32, [4]> var_3910 = const()[name = tensor<string, []>("op_3910"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_127 = reshape(shape = var_3910, x = var_3909)[name = tensor<string, []>("q_127")];
            tensor<fp32, [1, 188, 1024]> var_3914 = linear(bias = encoder_layers_21_self_attn_linear_k_bias, weight = encoder_layers_21_self_attn_linear_k_weight_quantized, x = query_43)[name = tensor<string, []>("linear_193")];
            tensor<int32, [4]> var_3915 = const()[name = tensor<string, []>("op_3915"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_85 = reshape(shape = var_3915, x = var_3914)[name = tensor<string, []>("k_85")];
            tensor<fp32, [1, 188, 1024]> var_3919 = linear(bias = encoder_layers_21_self_attn_linear_v_bias, weight = encoder_layers_21_self_attn_linear_v_weight_quantized, x = query_43)[name = tensor<string, []>("linear_194")];
            tensor<int32, [4]> var_3920 = const()[name = tensor<string, []>("op_3920"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_43 = reshape(shape = var_3920, x = var_3919)[name = tensor<string, []>("v_43")];
            tensor<int32, [4]> value_45_perm_0 = const()[name = tensor<string, []>("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_3932 = add(x = q_127, y = encoder_layers_21_self_attn_pos_bias_u)[name = tensor<string, []>("op_3932")];
            tensor<fp32, [1, 188, 8, 128]> var_3934 = add(x = q_127, y = encoder_layers_21_self_attn_pos_bias_v)[name = tensor<string, []>("op_3934")];
            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_3936_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3936_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596175424))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596559936))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596559488)))];
            tensor<bool, []> x_471_transpose_x_0 = const()[name = tensor<string, []>("x_471_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_471_transpose_y_0 = const()[name = tensor<string, []>("x_471_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_43 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3934)[name = tensor<string, []>("transpose_164")];
            tensor<fp32, [1, 8, 188, 375]> x_471 = matmul(transpose_x = x_471_transpose_x_0, transpose_y = x_471_transpose_y_0, x = q_with_bias_v_43, y = op_3936_quantized)[name = tensor<string, []>("x_471")];
            tensor<fp32, []> const_224 = const()[name = tensor<string, []>("const_224"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_473_pad_0 = const()[name = tensor<string, []>("x_473_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_473_mode_0 = const()[name = tensor<string, []>("x_473_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_473 = pad(constant_val = const_224, mode = x_473_mode_0, pad = x_473_pad_0, x = x_471)[name = tensor<string, []>("x_473")];
            tensor<int32, [4]> var_3944 = const()[name = tensor<string, []>("op_3944"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_475 = reshape(shape = var_3944, x = x_473)[name = tensor<string, []>("x_475")];
            tensor<int32, [4]> var_3948_begin_0 = const()[name = tensor<string, []>("op_3948_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3948_end_0 = const()[name = tensor<string, []>("op_3948_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3948_end_mask_0 = const()[name = tensor<string, []>("op_3948_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_3948 = slice_by_index(begin = var_3948_begin_0, end = var_3948_end_0, end_mask = var_3948_end_mask_0, x = x_475)[name = tensor<string, []>("op_3948")];
            tensor<int32, [4]> var_3949 = const()[name = tensor<string, []>("op_3949"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_85 = reshape(shape = var_3949, x = var_3948)[name = tensor<string, []>("matrix_bd_85")];
            tensor<bool, []> matrix_ac_43_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_43_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_43_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_138_perm_0 = const()[name = tensor<string, []>("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_139_perm_0 = const()[name = tensor<string, []>("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85)[name = tensor<string, []>("transpose_162")];
            tensor<fp32, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3932)[name = tensor<string, []>("transpose_163")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_43 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = tensor<string, []>("matrix_ac_43")];
            tensor<int32, [4]> matrix_bd_87_begin_0 = const()[name = tensor<string, []>("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = tensor<string, []>("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_87 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85)[name = tensor<string, []>("matrix_bd_87")];
            tensor<fp32, [1, 8, 188, 188]> var_3958 = add(x = matrix_ac_43, y = matrix_bd_87)[name = tensor<string, []>("op_3958")];
            tensor<fp32, []> _inversed_scores_85_y_0 = const()[name = tensor<string, []>("_inversed_scores_85_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_85 = mul(x = var_3958, y = _inversed_scores_85_y_0)[name = tensor<string, []>("_inversed_scores_85")];
            tensor<fp32, [1, 8, 188, 188]> scores_87 = select(a = var_14, b = _inversed_scores_85, cond = mask_3)[name = tensor<string, []>("scores_87")];
            tensor<fp32, [1, 8, 188, 188]> var_3964 = softmax(axis = var_32, x = scores_87)[name = tensor<string, []>("op_3964")];
            tensor<fp32, [1, 8, 188, 188]> input_1125 = select(a = var_13, b = var_3964, cond = mask_3)[name = tensor<string, []>("input_1125")];
            tensor<bool, []> x_477_transpose_x_0 = const()[name = tensor<string, []>("x_477_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_477_transpose_y_0 = const()[name = tensor<string, []>("x_477_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_45 = transpose(perm = value_45_perm_0, x = v_43)[name = tensor<string, []>("transpose_161")];
            tensor<fp32, [1, 8, 188, 128]> x_477 = matmul(transpose_x = x_477_transpose_x_0, transpose_y = x_477_transpose_y_0, x = input_1125, y = value_45)[name = tensor<string, []>("x_477")];
            tensor<int32, [4]> var_3968_perm_0 = const()[name = tensor<string, []>("op_3968_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3969 = const()[name = tensor<string, []>("op_3969"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_3968 = transpose(perm = var_3968_perm_0, x = x_477)[name = tensor<string, []>("transpose_160")];
            tensor<fp32, [1, 188, 1024]> input_1127 = reshape(shape = var_3969, x = var_3968)[name = tensor<string, []>("input_1127")];
            tensor<fp32, [1, 188, 1024]> input_1129 = linear(bias = encoder_layers_21_self_attn_linear_out_bias, weight = encoder_layers_21_self_attn_linear_out_weight_quantized, x = input_1127)[name = tensor<string, []>("linear_196")];
            tensor<fp32, [1, 188, 1024]> input_1131 = add(x = input_1123, y = input_1129)[name = tensor<string, []>("input_1131")];
            tensor<int32, [1]> x_481_axes_0 = const()[name = tensor<string, []>("x_481_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_481 = layer_norm(axes = x_481_axes_0, beta = encoder_layers_21_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_21_norm_conv_weight, x = input_1131)[name = tensor<string, []>("x_481")];
            tensor<int32, [3]> input_1133_perm_0 = const()[name = tensor<string, []>("input_1133_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1135_pad_type_0 = const()[name = tensor<string, []>("input_1135_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1135_strides_0 = const()[name = tensor<string, []>("input_1135_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1135_pad_0 = const()[name = tensor<string, []>("input_1135_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1135_dilations_0 = const()[name = tensor<string, []>("input_1135_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1135_groups_0 = const()[name = tensor<string, []>("input_1135_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_1133 = transpose(perm = input_1133_perm_0, x = x_481)[name = tensor<string, []>("transpose_159")];
            tensor<fp32, [1, 2048, 188]> input_1135 = conv(bias = encoder_layers_21_conv_pointwise_conv1_bias, dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = encoder_layers_21_conv_pointwise_conv1_weight_quantized, x = input_1133)[name = tensor<string, []>("input_1135")];
            tensor<int32, []> x_483_split_num_splits_0 = const()[name = tensor<string, []>("x_483_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_483_split_axis_0 = const()[name = tensor<string, []>("x_483_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_483_split_0, tensor<fp32, [1, 1024, 188]> x_483_split_1 = split(axis = x_483_split_axis_0, num_splits = x_483_split_num_splits_0, x = input_1135)[name = tensor<string, []>("x_483_split")];
            tensor<fp32, [1, 1024, 188]> x_483_split_1_sigmoid = sigmoid(x = x_483_split_1)[name = tensor<string, []>("x_483_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_483 = mul(x = x_483_split_0, y = x_483_split_1_sigmoid)[name = tensor<string, []>("x_483")];
            tensor<fp32, [1, 1024, 188]> input_1137 = select(a = var_13, b = x_483, cond = var_339)[name = tensor<string, []>("input_1137")];
            tensor<fp32, []> const_227 = const()[name = tensor<string, []>("const_227"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_1139_pad_0 = const()[name = tensor<string, []>("input_1139_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1139_mode_0 = const()[name = tensor<string, []>("input_1139_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_1139 = pad(constant_val = const_227, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137)[name = tensor<string, []>("input_1139")];
            tensor<string, []> input_1141_pad_type_0 = const()[name = tensor<string, []>("input_1141_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1141_groups_0 = const()[name = tensor<string, []>("input_1141_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1141_strides_0 = const()[name = tensor<string, []>("input_1141_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1141_pad_0 = const()[name = tensor<string, []>("input_1141_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1141_dilations_0 = const()[name = tensor<string, []>("input_1141_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_290_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_290_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596561536))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596571904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596570816)))];
            tensor<fp32, [1024]> const_291 = const()[name = tensor<string, []>("const_291"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596576064)))];
            tensor<fp32, [1, 1024, 188]> input_1143 = conv(bias = const_291, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_quantized, x = input_1139)[name = tensor<string, []>("input_1143")];
            tensor<fp32, [1, 1024, 188]> input_1145 = silu(x = input_1143)[name = tensor<string, []>("input_1145")];
            tensor<string, []> x_485_pad_type_0 = const()[name = tensor<string, []>("x_485_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_485_strides_0 = const()[name = tensor<string, []>("x_485_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_485_pad_0 = const()[name = tensor<string, []>("x_485_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_485_dilations_0 = const()[name = tensor<string, []>("x_485_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_485_groups_0 = const()[name = tensor<string, []>("x_485_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_485 = conv(bias = encoder_layers_21_conv_pointwise_conv2_bias, dilations = x_485_dilations_0, groups = x_485_groups_0, pad = x_485_pad_0, pad_type = x_485_pad_type_0, strides = x_485_strides_0, weight = encoder_layers_21_conv_pointwise_conv2_weight_quantized, x = input_1145)[name = tensor<string, []>("x_485")];
            tensor<int32, [3]> input_1147_perm_0 = const()[name = tensor<string, []>("input_1147_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_1147 = transpose(perm = input_1147_perm_0, x = x_485)[name = tensor<string, []>("transpose_158")];
            tensor<fp32, [1, 188, 1024]> input_1149 = add(x = input_1131, y = input_1147)[name = tensor<string, []>("input_1149")];
            tensor<int32, [1]> input_1151_axes_0 = const()[name = tensor<string, []>("input_1151_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1151 = layer_norm(axes = input_1151_axes_0, beta = encoder_layers_21_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_21_norm_feed_forward2_weight, x = input_1149)[name = tensor<string, []>("input_1151")];
            tensor<fp32, [1, 188, 4096]> input_1153 = linear(bias = encoder_layers_21_feed_forward2_linear1_bias, weight = encoder_layers_21_feed_forward2_linear1_weight_quantized, x = input_1151)[name = tensor<string, []>("linear_197")];
            tensor<fp32, [1, 188, 4096]> input_1155 = silu(x = input_1153)[name = tensor<string, []>("input_1155")];
            tensor<fp32, [1, 188, 1024]> input_1159 = linear(bias = encoder_layers_21_feed_forward2_linear2_bias, weight = encoder_layers_21_feed_forward2_linear2_weight_quantized, x = input_1155)[name = tensor<string, []>("linear_198")];
            tensor<fp32, []> var_4035 = const()[name = tensor<string, []>("op_4035"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_4036 = mul(x = input_1159, y = var_4035)[name = tensor<string, []>("op_4036")];
            tensor<fp32, [1, 188, 1024]> input_1161 = add(x = input_1149, y = var_4036)[name = tensor<string, []>("input_1161")];
            tensor<int32, [1]> input_1163_axes_0 = const()[name = tensor<string, []>("input_1163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1163 = layer_norm(axes = input_1163_axes_0, beta = encoder_layers_21_norm_out_bias, epsilon = var_11, gamma = encoder_layers_21_norm_out_weight, x = input_1161)[name = tensor<string, []>("input_1163")];
            tensor<int32, [1]> input_1165_axes_0 = const()[name = tensor<string, []>("input_1165_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1165 = layer_norm(axes = input_1165_axes_0, beta = encoder_layers_22_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_22_norm_feed_forward1_weight, x = input_1163)[name = tensor<string, []>("input_1165")];
            tensor<fp32, [1, 188, 4096]> input_1167 = linear(bias = encoder_layers_22_feed_forward1_linear1_bias, weight = encoder_layers_22_feed_forward1_linear1_weight_quantized, x = input_1165)[name = tensor<string, []>("linear_199")];
            tensor<fp32, [1, 188, 4096]> input_1169 = silu(x = input_1167)[name = tensor<string, []>("input_1169")];
            tensor<fp32, [1, 188, 1024]> input_1173 = linear(bias = encoder_layers_22_feed_forward1_linear2_bias, weight = encoder_layers_22_feed_forward1_linear2_weight_quantized, x = input_1169)[name = tensor<string, []>("linear_200")];
            tensor<fp32, []> var_4066 = const()[name = tensor<string, []>("op_4066"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_4067 = mul(x = input_1173, y = var_4066)[name = tensor<string, []>("op_4067")];
            tensor<fp32, [1, 188, 1024]> input_1175 = add(x = input_1163, y = var_4067)[name = tensor<string, []>("input_1175")];
            tensor<int32, [1]> query_45_axes_0 = const()[name = tensor<string, []>("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query_45 = layer_norm(axes = query_45_axes_0, beta = encoder_layers_22_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_22_norm_self_att_weight, x = input_1175)[name = tensor<string, []>("query_45")];
            tensor<fp32, [1, 188, 1024]> var_4083 = linear(bias = encoder_layers_22_self_attn_linear_q_bias, weight = encoder_layers_22_self_attn_linear_q_weight_quantized, x = query_45)[name = tensor<string, []>("linear_201")];
            tensor<int32, [4]> var_4084 = const()[name = tensor<string, []>("op_4084"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_133 = reshape(shape = var_4084, x = var_4083)[name = tensor<string, []>("q_133")];
            tensor<fp32, [1, 188, 1024]> var_4088 = linear(bias = encoder_layers_22_self_attn_linear_k_bias, weight = encoder_layers_22_self_attn_linear_k_weight_quantized, x = query_45)[name = tensor<string, []>("linear_202")];
            tensor<int32, [4]> var_4089 = const()[name = tensor<string, []>("op_4089"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_89 = reshape(shape = var_4089, x = var_4088)[name = tensor<string, []>("k_89")];
            tensor<fp32, [1, 188, 1024]> var_4093 = linear(bias = encoder_layers_22_self_attn_linear_v_bias, weight = encoder_layers_22_self_attn_linear_v_weight_quantized, x = query_45)[name = tensor<string, []>("linear_203")];
            tensor<int32, [4]> var_4094 = const()[name = tensor<string, []>("op_4094"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v_45 = reshape(shape = var_4094, x = var_4093)[name = tensor<string, []>("v_45")];
            tensor<int32, [4]> value_47_perm_0 = const()[name = tensor<string, []>("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_4106 = add(x = q_133, y = encoder_layers_22_self_attn_pos_bias_u)[name = tensor<string, []>("op_4106")];
            tensor<fp32, [1, 188, 8, 128]> var_4108 = add(x = q_133, y = encoder_layers_22_self_attn_pos_bias_v)[name = tensor<string, []>("op_4108")];
            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_4110_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_4110_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596580224))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596964736))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596964288)))];
            tensor<bool, []> x_493_transpose_x_0 = const()[name = tensor<string, []>("x_493_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_493_transpose_y_0 = const()[name = tensor<string, []>("x_493_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v_45 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4108)[name = tensor<string, []>("transpose_157")];
            tensor<fp32, [1, 8, 188, 375]> x_493 = matmul(transpose_x = x_493_transpose_x_0, transpose_y = x_493_transpose_y_0, x = q_with_bias_v_45, y = op_4110_quantized)[name = tensor<string, []>("x_493")];
            tensor<fp32, []> const_234 = const()[name = tensor<string, []>("const_234"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_495_pad_0 = const()[name = tensor<string, []>("x_495_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_495_mode_0 = const()[name = tensor<string, []>("x_495_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_495 = pad(constant_val = const_234, mode = x_495_mode_0, pad = x_495_pad_0, x = x_493)[name = tensor<string, []>("x_495")];
            tensor<int32, [4]> var_4118 = const()[name = tensor<string, []>("op_4118"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_497 = reshape(shape = var_4118, x = x_495)[name = tensor<string, []>("x_497")];
            tensor<int32, [4]> var_4122_begin_0 = const()[name = tensor<string, []>("op_4122_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_4122_end_0 = const()[name = tensor<string, []>("op_4122_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_4122_end_mask_0 = const()[name = tensor<string, []>("op_4122_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_4122 = slice_by_index(begin = var_4122_begin_0, end = var_4122_end_0, end_mask = var_4122_end_mask_0, x = x_497)[name = tensor<string, []>("op_4122")];
            tensor<int32, [4]> var_4123 = const()[name = tensor<string, []>("op_4123"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_89 = reshape(shape = var_4123, x = var_4122)[name = tensor<string, []>("matrix_bd_89")];
            tensor<bool, []> matrix_ac_45_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_45_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_45_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_45_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_140_perm_0 = const()[name = tensor<string, []>("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_141_perm_0 = const()[name = tensor<string, []>("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89)[name = tensor<string, []>("transpose_155")];
            tensor<fp32, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4106)[name = tensor<string, []>("transpose_156")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac_45 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = tensor<string, []>("matrix_ac_45")];
            tensor<int32, [4]> matrix_bd_91_begin_0 = const()[name = tensor<string, []>("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = tensor<string, []>("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd_91 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89)[name = tensor<string, []>("matrix_bd_91")];
            tensor<fp32, [1, 8, 188, 188]> var_4132 = add(x = matrix_ac_45, y = matrix_bd_91)[name = tensor<string, []>("op_4132")];
            tensor<fp32, []> _inversed_scores_89_y_0 = const()[name = tensor<string, []>("_inversed_scores_89_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_89 = mul(x = var_4132, y = _inversed_scores_89_y_0)[name = tensor<string, []>("_inversed_scores_89")];
            tensor<fp32, [1, 8, 188, 188]> scores_91 = select(a = var_14, b = _inversed_scores_89, cond = mask_3)[name = tensor<string, []>("scores_91")];
            tensor<fp32, [1, 8, 188, 188]> var_4138 = softmax(axis = var_32, x = scores_91)[name = tensor<string, []>("op_4138")];
            tensor<fp32, [1, 8, 188, 188]> input_1177 = select(a = var_13, b = var_4138, cond = mask_3)[name = tensor<string, []>("input_1177")];
            tensor<bool, []> x_499_transpose_x_0 = const()[name = tensor<string, []>("x_499_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_499_transpose_y_0 = const()[name = tensor<string, []>("x_499_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value_47 = transpose(perm = value_47_perm_0, x = v_45)[name = tensor<string, []>("transpose_154")];
            tensor<fp32, [1, 8, 188, 128]> x_499 = matmul(transpose_x = x_499_transpose_x_0, transpose_y = x_499_transpose_y_0, x = input_1177, y = value_47)[name = tensor<string, []>("x_499")];
            tensor<int32, [4]> var_4142_perm_0 = const()[name = tensor<string, []>("op_4142_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_4143 = const()[name = tensor<string, []>("op_4143"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_4142 = transpose(perm = var_4142_perm_0, x = x_499)[name = tensor<string, []>("transpose_153")];
            tensor<fp32, [1, 188, 1024]> input_1179 = reshape(shape = var_4143, x = var_4142)[name = tensor<string, []>("input_1179")];
            tensor<fp32, [1, 188, 1024]> input_1181 = linear(bias = encoder_layers_22_self_attn_linear_out_bias, weight = encoder_layers_22_self_attn_linear_out_weight_quantized, x = input_1179)[name = tensor<string, []>("linear_205")];
            tensor<fp32, [1, 188, 1024]> input_1183 = add(x = input_1175, y = input_1181)[name = tensor<string, []>("input_1183")];
            tensor<int32, [1]> x_503_axes_0 = const()[name = tensor<string, []>("x_503_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_503 = layer_norm(axes = x_503_axes_0, beta = encoder_layers_22_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_22_norm_conv_weight, x = input_1183)[name = tensor<string, []>("x_503")];
            tensor<int32, [3]> input_1185_perm_0 = const()[name = tensor<string, []>("input_1185_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1187_pad_type_0 = const()[name = tensor<string, []>("input_1187_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1187_strides_0 = const()[name = tensor<string, []>("input_1187_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1187_pad_0 = const()[name = tensor<string, []>("input_1187_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1187_dilations_0 = const()[name = tensor<string, []>("input_1187_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1187_groups_0 = const()[name = tensor<string, []>("input_1187_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_1185 = transpose(perm = input_1185_perm_0, x = x_503)[name = tensor<string, []>("transpose_152")];
            tensor<fp32, [1, 2048, 188]> input_1187 = conv(bias = encoder_layers_22_conv_pointwise_conv1_bias, dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = encoder_layers_22_conv_pointwise_conv1_weight_quantized, x = input_1185)[name = tensor<string, []>("input_1187")];
            tensor<int32, []> x_505_split_num_splits_0 = const()[name = tensor<string, []>("x_505_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_505_split_axis_0 = const()[name = tensor<string, []>("x_505_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_505_split_0, tensor<fp32, [1, 1024, 188]> x_505_split_1 = split(axis = x_505_split_axis_0, num_splits = x_505_split_num_splits_0, x = input_1187)[name = tensor<string, []>("x_505_split")];
            tensor<fp32, [1, 1024, 188]> x_505_split_1_sigmoid = sigmoid(x = x_505_split_1)[name = tensor<string, []>("x_505_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_505 = mul(x = x_505_split_0, y = x_505_split_1_sigmoid)[name = tensor<string, []>("x_505")];
            tensor<fp32, [1, 1024, 188]> input_1189 = select(a = var_13, b = x_505, cond = var_339)[name = tensor<string, []>("input_1189")];
            tensor<fp32, []> const_237 = const()[name = tensor<string, []>("const_237"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_1191_pad_0 = const()[name = tensor<string, []>("input_1191_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1191_mode_0 = const()[name = tensor<string, []>("input_1191_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_1191 = pad(constant_val = const_237, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189)[name = tensor<string, []>("input_1191")];
            tensor<string, []> input_1193_pad_type_0 = const()[name = tensor<string, []>("input_1193_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1193_groups_0 = const()[name = tensor<string, []>("input_1193_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1193_strides_0 = const()[name = tensor<string, []>("input_1193_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1193_pad_0 = const()[name = tensor<string, []>("input_1193_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1193_dilations_0 = const()[name = tensor<string, []>("input_1193_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_292_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_292_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596966336))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596976704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596975616)))];
            tensor<fp32, [1024]> const_293 = const()[name = tensor<string, []>("const_293"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596980864)))];
            tensor<fp32, [1, 1024, 188]> input_1195 = conv(bias = const_293, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_quantized, x = input_1191)[name = tensor<string, []>("input_1195")];
            tensor<fp32, [1, 1024, 188]> input_1197 = silu(x = input_1195)[name = tensor<string, []>("input_1197")];
            tensor<string, []> x_507_pad_type_0 = const()[name = tensor<string, []>("x_507_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_507_strides_0 = const()[name = tensor<string, []>("x_507_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_507_pad_0 = const()[name = tensor<string, []>("x_507_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_507_dilations_0 = const()[name = tensor<string, []>("x_507_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_507_groups_0 = const()[name = tensor<string, []>("x_507_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_507 = conv(bias = encoder_layers_22_conv_pointwise_conv2_bias, dilations = x_507_dilations_0, groups = x_507_groups_0, pad = x_507_pad_0, pad_type = x_507_pad_type_0, strides = x_507_strides_0, weight = encoder_layers_22_conv_pointwise_conv2_weight_quantized, x = input_1197)[name = tensor<string, []>("x_507")];
            tensor<int32, [3]> input_1199_perm_0 = const()[name = tensor<string, []>("input_1199_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_1199 = transpose(perm = input_1199_perm_0, x = x_507)[name = tensor<string, []>("transpose_151")];
            tensor<fp32, [1, 188, 1024]> input_1201 = add(x = input_1183, y = input_1199)[name = tensor<string, []>("input_1201")];
            tensor<int32, [1]> input_1203_axes_0 = const()[name = tensor<string, []>("input_1203_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1203 = layer_norm(axes = input_1203_axes_0, beta = encoder_layers_22_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_22_norm_feed_forward2_weight, x = input_1201)[name = tensor<string, []>("input_1203")];
            tensor<fp32, [1, 188, 4096]> input_1205 = linear(bias = encoder_layers_22_feed_forward2_linear1_bias, weight = encoder_layers_22_feed_forward2_linear1_weight_quantized, x = input_1203)[name = tensor<string, []>("linear_206")];
            tensor<fp32, [1, 188, 4096]> input_1207 = silu(x = input_1205)[name = tensor<string, []>("input_1207")];
            tensor<fp32, [1, 188, 1024]> input_1211 = linear(bias = encoder_layers_22_feed_forward2_linear2_bias, weight = encoder_layers_22_feed_forward2_linear2_weight_quantized, x = input_1207)[name = tensor<string, []>("linear_207")];
            tensor<fp32, []> var_4209 = const()[name = tensor<string, []>("op_4209"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_4210 = mul(x = input_1211, y = var_4209)[name = tensor<string, []>("op_4210")];
            tensor<fp32, [1, 188, 1024]> input_1213 = add(x = input_1201, y = var_4210)[name = tensor<string, []>("input_1213")];
            tensor<int32, [1]> input_1215_axes_0 = const()[name = tensor<string, []>("input_1215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1215 = layer_norm(axes = input_1215_axes_0, beta = encoder_layers_22_norm_out_bias, epsilon = var_11, gamma = encoder_layers_22_norm_out_weight, x = input_1213)[name = tensor<string, []>("input_1215")];
            tensor<int32, [1]> input_1217_axes_0 = const()[name = tensor<string, []>("input_1217_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1217 = layer_norm(axes = input_1217_axes_0, beta = encoder_layers_23_norm_feed_forward1_bias, epsilon = var_11, gamma = encoder_layers_23_norm_feed_forward1_weight, x = input_1215)[name = tensor<string, []>("input_1217")];
            tensor<fp32, [1, 188, 4096]> input_1219 = linear(bias = encoder_layers_23_feed_forward1_linear1_bias, weight = encoder_layers_23_feed_forward1_linear1_weight_quantized, x = input_1217)[name = tensor<string, []>("linear_208")];
            tensor<fp32, [1, 188, 4096]> input_1221 = silu(x = input_1219)[name = tensor<string, []>("input_1221")];
            tensor<fp32, [1, 188, 1024]> input_1225 = linear(bias = encoder_layers_23_feed_forward1_linear2_bias, weight = encoder_layers_23_feed_forward1_linear2_weight_quantized, x = input_1221)[name = tensor<string, []>("linear_209")];
            tensor<fp32, []> var_4240 = const()[name = tensor<string, []>("op_4240"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_4241 = mul(x = input_1225, y = var_4240)[name = tensor<string, []>("op_4241")];
            tensor<fp32, [1, 188, 1024]> input_1227 = add(x = input_1215, y = var_4241)[name = tensor<string, []>("input_1227")];
            tensor<int32, [1]> query_axes_0 = const()[name = tensor<string, []>("query_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> query = layer_norm(axes = query_axes_0, beta = encoder_layers_23_norm_self_att_bias, epsilon = var_11, gamma = encoder_layers_23_norm_self_att_weight, x = input_1227)[name = tensor<string, []>("query")];
            tensor<fp32, [1, 188, 1024]> var_4257 = linear(bias = encoder_layers_23_self_attn_linear_q_bias, weight = encoder_layers_23_self_attn_linear_q_weight_quantized, x = query)[name = tensor<string, []>("linear_210")];
            tensor<int32, [4]> var_4258 = const()[name = tensor<string, []>("op_4258"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> q_139 = reshape(shape = var_4258, x = var_4257)[name = tensor<string, []>("q_139")];
            tensor<fp32, [1, 188, 1024]> var_4262 = linear(bias = encoder_layers_23_self_attn_linear_k_bias, weight = encoder_layers_23_self_attn_linear_k_weight_quantized, x = query)[name = tensor<string, []>("linear_211")];
            tensor<int32, [4]> var_4263 = const()[name = tensor<string, []>("op_4263"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> k_93 = reshape(shape = var_4263, x = var_4262)[name = tensor<string, []>("k_93")];
            tensor<fp32, [1, 188, 1024]> var_4267 = linear(bias = encoder_layers_23_self_attn_linear_v_bias, weight = encoder_layers_23_self_attn_linear_v_weight_quantized, x = query)[name = tensor<string, []>("linear_212")];
            tensor<int32, [4]> var_4268 = const()[name = tensor<string, []>("op_4268"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp32, [1, 188, 8, 128]> v = reshape(shape = var_4268, x = var_4267)[name = tensor<string, []>("v")];
            tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 188, 8, 128]> var_4280 = add(x = q_139, y = encoder_layers_23_self_attn_pos_bias_u)[name = tensor<string, []>("op_4280")];
            tensor<fp32, [1, 188, 8, 128]> var_4282 = add(x = q_139, y = encoder_layers_23_self_attn_pos_bias_v)[name = tensor<string, []>("op_4282")];
            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp32, [1, 8, 128, 375]> op_4284_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_4284_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596985024))), scale = tensor<fp32, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597369536))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597369088)))];
            tensor<bool, []> x_515_transpose_x_0 = const()[name = tensor<string, []>("x_515_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_515_transpose_y_0 = const()[name = tensor<string, []>("x_515_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> q_with_bias_v = transpose(perm = q_with_bias_v_perm_0, x = var_4282)[name = tensor<string, []>("transpose_150")];
            tensor<fp32, [1, 8, 188, 375]> x_515 = matmul(transpose_x = x_515_transpose_x_0, transpose_y = x_515_transpose_y_0, x = q_with_bias_v, y = op_4284_quantized)[name = tensor<string, []>("x_515")];
            tensor<fp32, []> const_244 = const()[name = tensor<string, []>("const_244"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> x_517_pad_0 = const()[name = tensor<string, []>("x_517_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_517_mode_0 = const()[name = tensor<string, []>("x_517_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 8, 188, 376]> x_517 = pad(constant_val = const_244, mode = x_517_mode_0, pad = x_517_pad_0, x = x_515)[name = tensor<string, []>("x_517")];
            tensor<int32, [4]> var_4292 = const()[name = tensor<string, []>("op_4292"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp32, [1, 8, 376, 188]> x_519 = reshape(shape = var_4292, x = x_517)[name = tensor<string, []>("x_519")];
            tensor<int32, [4]> var_4296_begin_0 = const()[name = tensor<string, []>("op_4296_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_4296_end_0 = const()[name = tensor<string, []>("op_4296_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_4296_end_mask_0 = const()[name = tensor<string, []>("op_4296_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 375, 188]> var_4296 = slice_by_index(begin = var_4296_begin_0, end = var_4296_end_0, end_mask = var_4296_end_mask_0, x = x_519)[name = tensor<string, []>("op_4296")];
            tensor<int32, [4]> var_4297 = const()[name = tensor<string, []>("op_4297"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp32, [1, 8, 188, 375]> matrix_bd_93 = reshape(shape = var_4297, x = var_4296)[name = tensor<string, []>("matrix_bd_93")];
            tensor<bool, []> matrix_ac_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_142_perm_0 = const()[name = tensor<string, []>("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_143_perm_0 = const()[name = tensor<string, []>("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 8, 128, 188]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93)[name = tensor<string, []>("transpose_148")];
            tensor<fp32, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4280)[name = tensor<string, []>("transpose_149")];
            tensor<fp32, [1, 8, 188, 188]> matrix_ac = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = tensor<string, []>("matrix_ac")];
            tensor<int32, [4]> matrix_bd_begin_0 = const()[name = tensor<string, []>("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_end_0 = const()[name = tensor<string, []>("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 188, 188]> matrix_bd = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93)[name = tensor<string, []>("matrix_bd")];
            tensor<fp32, [1, 8, 188, 188]> var_4306 = add(x = matrix_ac, y = matrix_bd)[name = tensor<string, []>("op_4306")];
            tensor<fp32, []> _inversed_scores_93_y_0 = const()[name = tensor<string, []>("_inversed_scores_93_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-4)];
            tensor<fp32, [1, 8, 188, 188]> _inversed_scores_93 = mul(x = var_4306, y = _inversed_scores_93_y_0)[name = tensor<string, []>("_inversed_scores_93")];
            tensor<fp32, [1, 8, 188, 188]> scores = select(a = var_14, b = _inversed_scores_93, cond = mask_3)[name = tensor<string, []>("scores")];
            tensor<fp32, [1, 8, 188, 188]> var_4312 = softmax(axis = var_32, x = scores)[name = tensor<string, []>("op_4312")];
            tensor<fp32, [1, 8, 188, 188]> input_1229 = select(a = var_13, b = var_4312, cond = mask_3)[name = tensor<string, []>("input_1229")];
            tensor<bool, []> x_521_transpose_x_0 = const()[name = tensor<string, []>("x_521_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_521_transpose_y_0 = const()[name = tensor<string, []>("x_521_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 8, 188, 128]> value = transpose(perm = value_perm_0, x = v)[name = tensor<string, []>("transpose_147")];
            tensor<fp32, [1, 8, 188, 128]> x_521 = matmul(transpose_x = x_521_transpose_x_0, transpose_y = x_521_transpose_y_0, x = input_1229, y = value)[name = tensor<string, []>("x_521")];
            tensor<int32, [4]> var_4316_perm_0 = const()[name = tensor<string, []>("op_4316_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_4317 = const()[name = tensor<string, []>("op_4317"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp32, [1, 188, 8, 128]> var_4316 = transpose(perm = var_4316_perm_0, x = x_521)[name = tensor<string, []>("transpose_146")];
            tensor<fp32, [1, 188, 1024]> input_1231 = reshape(shape = var_4317, x = var_4316)[name = tensor<string, []>("input_1231")];
            tensor<fp32, [1, 188, 1024]> input_1233 = linear(bias = encoder_layers_23_self_attn_linear_out_bias, weight = encoder_layers_23_self_attn_linear_out_weight_quantized, x = input_1231)[name = tensor<string, []>("linear_214")];
            tensor<fp32, [1, 188, 1024]> input_1235 = add(x = input_1227, y = input_1233)[name = tensor<string, []>("input_1235")];
            tensor<int32, [1]> x_525_axes_0 = const()[name = tensor<string, []>("x_525_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> x_525 = layer_norm(axes = x_525_axes_0, beta = encoder_layers_23_norm_conv_bias, epsilon = var_11, gamma = encoder_layers_23_norm_conv_weight, x = input_1235)[name = tensor<string, []>("x_525")];
            tensor<int32, [3]> input_1237_perm_0 = const()[name = tensor<string, []>("input_1237_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1239_pad_type_0 = const()[name = tensor<string, []>("input_1239_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1239_strides_0 = const()[name = tensor<string, []>("input_1239_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1239_pad_0 = const()[name = tensor<string, []>("input_1239_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1239_dilations_0 = const()[name = tensor<string, []>("input_1239_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1239_groups_0 = const()[name = tensor<string, []>("input_1239_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> input_1237 = transpose(perm = input_1237_perm_0, x = x_525)[name = tensor<string, []>("transpose_145")];
            tensor<fp32, [1, 2048, 188]> input_1239 = conv(bias = encoder_layers_23_conv_pointwise_conv1_bias, dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = encoder_layers_23_conv_pointwise_conv1_weight_quantized, x = input_1237)[name = tensor<string, []>("input_1239")];
            tensor<int32, []> x_527_split_num_splits_0 = const()[name = tensor<string, []>("x_527_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_527_split_axis_0 = const()[name = tensor<string, []>("x_527_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_527_split_0, tensor<fp32, [1, 1024, 188]> x_527_split_1 = split(axis = x_527_split_axis_0, num_splits = x_527_split_num_splits_0, x = input_1239)[name = tensor<string, []>("x_527_split")];
            tensor<fp32, [1, 1024, 188]> x_527_split_1_sigmoid = sigmoid(x = x_527_split_1)[name = tensor<string, []>("x_527_split_1_sigmoid")];
            tensor<fp32, [1, 1024, 188]> x_527 = mul(x = x_527_split_0, y = x_527_split_1_sigmoid)[name = tensor<string, []>("x_527")];
            tensor<fp32, [1, 1024, 188]> input_1241 = select(a = var_13, b = x_527, cond = var_339)[name = tensor<string, []>("input_1241")];
            tensor<fp32, []> const_247 = const()[name = tensor<string, []>("const_247"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> input_1243_pad_0 = const()[name = tensor<string, []>("input_1243_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1243_mode_0 = const()[name = tensor<string, []>("input_1243_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1024, 196]> input_1243 = pad(constant_val = const_247, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241)[name = tensor<string, []>("input_1243")];
            tensor<string, []> input_1245_pad_type_0 = const()[name = tensor<string, []>("input_1245_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1245_groups_0 = const()[name = tensor<string, []>("input_1245_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1245_strides_0 = const()[name = tensor<string, []>("input_1245_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1245_pad_0 = const()[name = tensor<string, []>("input_1245_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1245_dilations_0 = const()[name = tensor<string, []>("input_1245_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1024, 1, 9]> const_294_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_294_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597371136))), scale = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597381504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597380416)))];
            tensor<fp32, [1024]> const_295 = const()[name = tensor<string, []>("const_295"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597385664)))];
            tensor<fp32, [1, 1024, 188]> input_1247 = conv(bias = const_295, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_quantized, x = input_1243)[name = tensor<string, []>("input_1247")];
            tensor<fp32, [1, 1024, 188]> input_1249 = silu(x = input_1247)[name = tensor<string, []>("input_1249")];
            tensor<string, []> x_529_pad_type_0 = const()[name = tensor<string, []>("x_529_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_529_strides_0 = const()[name = tensor<string, []>("x_529_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_529_pad_0 = const()[name = tensor<string, []>("x_529_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_529_dilations_0 = const()[name = tensor<string, []>("x_529_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_529_groups_0 = const()[name = tensor<string, []>("x_529_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 188]> x_529 = conv(bias = encoder_layers_23_conv_pointwise_conv2_bias, dilations = x_529_dilations_0, groups = x_529_groups_0, pad = x_529_pad_0, pad_type = x_529_pad_type_0, strides = x_529_strides_0, weight = encoder_layers_23_conv_pointwise_conv2_weight_quantized, x = input_1249)[name = tensor<string, []>("x_529")];
            tensor<int32, [3]> input_1251_perm_0 = const()[name = tensor<string, []>("input_1251_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 188, 1024]> input_1251 = transpose(perm = input_1251_perm_0, x = x_529)[name = tensor<string, []>("transpose_144")];
            tensor<fp32, [1, 188, 1024]> input_1253 = add(x = input_1235, y = input_1251)[name = tensor<string, []>("input_1253")];
            tensor<int32, [1]> input_1255_axes_0 = const()[name = tensor<string, []>("input_1255_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> input_1255 = layer_norm(axes = input_1255_axes_0, beta = encoder_layers_23_norm_feed_forward2_bias, epsilon = var_11, gamma = encoder_layers_23_norm_feed_forward2_weight, x = input_1253)[name = tensor<string, []>("input_1255")];
            tensor<fp32, [1, 188, 4096]> input_1257 = linear(bias = encoder_layers_23_feed_forward2_linear1_bias, weight = encoder_layers_23_feed_forward2_linear1_weight_quantized, x = input_1255)[name = tensor<string, []>("linear_215")];
            tensor<fp32, [1, 188, 4096]> input_1259 = silu(x = input_1257)[name = tensor<string, []>("input_1259")];
            tensor<fp32, [1, 188, 1024]> input_1263 = linear(bias = encoder_layers_23_feed_forward2_linear2_bias, weight = encoder_layers_23_feed_forward2_linear2_weight_quantized, x = input_1259)[name = tensor<string, []>("linear_216")];
            tensor<fp32, []> var_4383 = const()[name = tensor<string, []>("op_4383"), val = tensor<fp32, []>(0x1p-1)];
            tensor<fp32, [1, 188, 1024]> var_4384 = mul(x = input_1263, y = var_4383)[name = tensor<string, []>("op_4384")];
            tensor<fp32, [1, 188, 1024]> input = add(x = input_1253, y = var_4384)[name = tensor<string, []>("input")];
            tensor<int32, [1]> audio_signal_axes_0 = const()[name = tensor<string, []>("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 188, 1024]> audio_signal = layer_norm(axes = audio_signal_axes_0, beta = encoder_layers_23_norm_out_bias, epsilon = var_11, gamma = encoder_layers_23_norm_out_weight, x = input)[name = tensor<string, []>("audio_signal")];
            tensor<fp32, [1, 188, 1025]> ctc_head_raw_output = linear(bias = proj_bias, weight = proj_weight_quantized, x = audio_signal)[name = tensor<string, []>("linear_217")];
        } -> (ctc_head_raw_output, encoder_length);
}